{"as_of":"2026-08-10T11:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6d4a32afdefca387bf15d8a1e5f4ece41690283d4924f8f6fc3f430b7f5c984d","coverage":[{"denominator":114,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:01:57.729474Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.11955/citation-record","integrity":"/paper/2507.11955/integrity","json":"/paper/2507.11955/citation-record.json","paper":"/paper/2507.11955"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:48.683215Z","title":"Threshold-adaptive unsu- pervised focal loss for domain adaptation of semantic segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:48.683215Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:ed8eeeea4fc6ae447c2a33ecf7c35864f3a536505e881431cdf3813cafd51d6f","observation_id":"e0377f81-dc07-4fc7-bdd7-ecbd2c27546a","resolution":{"observed_at":"2026-08-06T17:01:48.683215Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:48.743818Z","title":"Sfnet-n: An improved sfnet algorithm for semantic segmentation of low-light autonomous driving road scenes,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:48.743818Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:0f158713927b619b7a83d470f7a0462aa86e266a9075f18a7978e254c0692cb3","observation_id":"77b00084-200a-4663-a03b-62b1bdb08f12","resolution":{"observed_at":"2026-08-06T17:01:48.743818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:48.861333Z","title":"Multiple relational learning network for joint referring expression comprehension and segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:48.861333Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:df066b9d7f9850224f5be2fa6521699559746c399c1d07765afebd280f6ec2da","observation_id":"72051996-9a10-404e-b7a5-515dc6474a18","resolution":{"observed_at":"2026-08-06T17:01:48.861333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:48.936528Z","title":"Contrastive tokens and label acti- vation for remote sensing weakly supervised semantic segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:48.936528Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:158440c1c50f2f1f0d7fcd231080b69152bc00a591de454facb72bdf673f8ebd","observation_id":"82f6f54c-b8d0-4979-909f-f72921c099fd","resolution":{"observed_at":"2026-08-06T17:01:48.936528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.011573Z","title":"Improving robustness of single image super-resolution models with monte carlo method,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.011573Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:bfe24b7f2695b35207c3571c941ae7fcf880fbd872ca308f7981cec5543a875e","observation_id":"731eefca-f296-4fa2-9086-aa5f8d70d6b0","resolution":{"observed_at":"2026-08-06T17:01:49.011573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.015817Z","title":"Token contrast for weakly- supervised semantic segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.015817Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:d986b87fdd0b372bb062af9f306ba7d5bdca046ff5ed1c5295eb2d8977b8ed9c","observation_id":"773eda0c-20f7-4e8b-98ba-cdf7ffedca10","resolution":{"observed_at":"2026-08-06T17:01:49.015817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.120310Z","title":"Exploring more concentrated and consistent activation regions for cross-domain semantic segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.120310Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:4a92ffcf63a9bceabca3800d0c5c4c73703afcabc3cb3a4fdd1b0872d96ba12a","observation_id":"62409aaf-5191-4301-8aff-f363f0004cd7","resolution":{"observed_at":"2026-08-06T17:01:49.120310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.226783Z","title":"Transfer beyond the field of view: Dense panoramic semantic segmentation via unsupervised domain adaptation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.226783Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:6e3ff42ea203dc56c69590b1945c8975f9413fd9be17c213910ae753051b1280","observation_id":"27ba8b51-0a37-4d03-96a6-18335e649649","resolution":{"observed_at":"2026-08-06T17:01:49.226783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.377832Z","title":"Dual geometric perception for cross-domain road segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.377832Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:f5b7d7579754435388347617bcd45ca923b801ae764ffb09f1b7e824296b882f","observation_id":"40b74346-30a9-47a4-9da0-2b355d293b1a","resolution":{"observed_at":"2026-08-06T17:01:49.377832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.454476Z","title":"Fda: Fourier domain adaptation for semantic segmentation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.454476Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:9a5276342d5be3c6af14f372b8ae203d35410168eb57c66c327624b115f1e032","observation_id":"48519e9b-5774-44d7-af63-1c04cefb4f4a","resolution":{"observed_at":"2026-08-06T17:01:49.454476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.569309Z","title":"Feature-based style randomization for domain generalization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.569309Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:e4e7401ee0c75de1476ca2966e99b269bae8e65286508143e5df3805aae2a126","observation_id":"c49c988f-0c83-455f-ab2f-db089fdb56be","resolution":{"observed_at":"2026-08-06T17:01:49.569309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.731907Z","title":"Generalizing to unseen domains: A survey on domain generalization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.731907Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:ba2e40e0a97839372cfc6e6cf74b1e39b55299f3c17ad6c37effcf4f4ee1ab9f","observation_id":"b890293a-2b40-4ffd-87ad-09db926901a0","resolution":{"observed_at":"2026-08-06T17:01:49.731907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.827129Z","title":"Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.827129Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:18f8d9a7c044f759244e55a0621dd4c3fceff57a6cbb2b87cde80aea4b3488d7","observation_id":"bc3dae32-c30b-47de-88f8-f5a666255e47","resolution":{"observed_at":"2026-08-06T17:01:49.827129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:49.911505Z","title":"Fsdr: Frequency space domain randomization for domain generalization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:49.911505Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:3690818559e4c6540499e939d186cfd24497760b7b6ed77dbb6a5ef1267e3a10","observation_id":"7f883354-1589-463c-9190-59c4f2fce5ce","resolution":{"observed_at":"2026-08-06T17:01:49.911505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.020502Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.020502Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:04ead899937ee7e3c549a561816ac670716b4142bddd8a929766119e63d8de0e","observation_id":"a0f3f244-c202-4cd8-9a55-95e5959ccf17","resolution":{"observed_at":"2026-08-06T17:01:50.020502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.097195Z","title":"Switchable whitening for deep representation learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.097195Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:4bc7d6ce7c0ce467fdb809356f6c466f18fe9ba4da7bfd9d50f51d707a58abf5","observation_id":"545ee539-250c-47cc-ac73-90f40ee0ed77","resolution":{"observed_at":"2026-08-06T17:01:50.097195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.189181Z","title":"Bapa-net: Boundary adaptation and prototype alignment for cross-domain semantic segmen- tation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.189181Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:f325b513c4e8539bba7f78af08894e17c6895e98e8d9adb2044f6093da73b870","observation_id":"70d23e57-a0b0-4413-8a93-697f3210d9d2","resolution":{"observed_at":"2026-08-06T17:01:50.189181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.268656Z","title":"Category anchor-guided unsupervised domain adaptation for semantic segmentation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.268656Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:f89ed48cf18a76efec6d63cbcee5554094b2ca9d1c1a1e6c333692e51ba69aef","observation_id":"7b7d4a61-0ca8-4932-97bc-f8df4d834658","resolution":{"observed_at":"2026-08-06T17:01:50.268656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.370514Z","title":"Proto- typical contrast adaptation for domain adaptive semantic segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.370514Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:f02de1fe0b6d0bcd8c2ae1ab979bd3e06497672b31e008f16264274a8915f76a","observation_id":"2106cefc-0535-4cf7-a6fc-a5e307cf42e5","resolution":{"observed_at":"2026-08-06T17:01:50.370514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.496813Z","title":"Bi-directional contrastive learning for domain adaptive semantic segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.496813Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:32e27eb6005ca384b6550d6ed5331a93fad82c27ccd036e68a56a699f5df113c","observation_id":"378179e1-a149-43a9-846f-dfb5e790df1e","resolution":{"observed_at":"2026-08-06T17:01:50.496813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.575467Z","title":"Image style transfer using convolutional neural networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.575467Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:d91aa919f311ef2ba10c59d2eb4daa2537f62f57e3173a2898500222acdefcac","observation_id":"ddbe7a33-2e4d-458b-8ce6-c818f044796a","resolution":{"observed_at":"2026-08-06T17:01:50.575467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.669069Z","title":"Fully convolutional adaptation networks for semantic segmentation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.669069Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:252074f848e5dab483271143bc1c3a676812cab6350e0dee8db90b740f8fccf7","observation_id":"1b775fc8-2631-4900-8d94-1e46c500ce94","resolution":{"observed_at":"2026-08-06T17:01:50.669069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.768193Z","title":"Contextual-relation consis- tent domain adaptation for semantic segmentation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.768193Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:b6231b1d5727914f8c879f5015702468f4f8c572e33959bd7f21e9259d981f76","observation_id":"e7889716-13ec-489d-a966-4d0301ffedab","resolution":{"observed_at":"2026-08-06T17:01:50.768193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.873269Z","title":"Scale variance minimization for unsupervised domain adaptation in image segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.873269Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:11579239ed38b179c024b6051534987dc6e4df13919fd9d10d292fde8db67322","observation_id":"46bd895a-715f-421e-936e-3ed552bb4bfc","resolution":{"observed_at":"2026-08-06T17:01:50.873269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:50.973472Z","title":"Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:50.973472Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:83b1ce8054d12e7363170ebc6e85a1ee409bce43e0ab73fc99146ac277c19be1","observation_id":"906d536f-8684-4d36-8242-b281170d2461","resolution":{"observed_at":"2026-08-06T17:01:50.973472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.062536Z","title":"Characterizing and avoiding negative transfer,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.062536Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:658947989704e31c2da314088eae9d4b312964f68d26123a96db1ee1b5627a62","observation_id":"56d6b4e3-c709-4616-be2b-e1b97bca231b","resolution":{"observed_at":"2026-08-06T17:01:51.062536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.159110Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.159110Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:3039ce13e9d16ba3b4039f3c04716a8d43fac57a850355aec41a5a95e3513dcb","observation_id":"92b84642-f11f-43b4-88d2-83baf9c75362","resolution":{"observed_at":"2026-08-06T17:01:51.159110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.272214Z","title":"Curriculum domain adaptation for semantic segmentation of urban scenes,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.272214Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:23c47162818754007a885d731fc1068efe4b0e1de9a3aa70404fe06c3f135776","observation_id":"57bccc50-9edc-482a-90a6-b75d0e67a0fc","resolution":{"observed_at":"2026-08-06T17:01:51.272214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.337727Z","title":"Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.337727Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:4fde71f4387967ae0bc2d8cc9a6654fa7bb2ae3cfec743a1bd2dff6e57b42f58","observation_id":"dc8ea4f4-c3d9-4229-aba1-db295e41a756","resolution":{"observed_at":"2026-08-06T17:01:51.337727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.398171Z","title":"Adversarial domain adaptation with domain mixup,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.398171Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:34353195a1ee640b12cbb387c80dc6358556676049ec05ad731f2dfa70009f15","observation_id":"e29b5100-1ba0-487e-8f0e-831514642e48","resolution":{"observed_at":"2026-08-06T17:01:51.398171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.480564Z","title":"Dual mixup regularized learning for adversarial domain adaptation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.480564Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:937bac79c0430f91e99415bc9653cd0995e40eda45afc0cda8dd4083ebb05efb","observation_id":"c28b8111-48cb-42fd-87c0-3c4b06d34c0f","resolution":{"observed_at":"2026-08-06T17:01:51.480564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.578672Z","title":"A hybrid domain learning framework for unsupervised semantic segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.578672Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:e4e5c9aff9d4d04f205e8bc5960012a3599e055038aa61cbddeaf6cef63a3d50","observation_id":"4f9e56a1-2663-462e-85f2-04d9370dac9d","resolution":{"observed_at":"2026-08-06T17:01:51.578672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.665259Z","title":"Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.665259Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:f8aa3060e16dae77f667601ab821e15f1468ef1915e3c7f65ea81c114c9db6e0","observation_id":"1c49a14d-a847-4f30-9aef-3a4d17946625","resolution":{"observed_at":"2026-08-06T17:01:51.665259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:51.727561Z","title":"Delivering arbitrary-modal semantic segmenta- tion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.727561Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:ba5e662da3e647253226129aa94a665b87053e10047b49c045310cc33174a735","observation_id":"6547dae3-9682-415c-b32b-92fc43690efb","resolution":{"observed_at":"2026-08-06T17:01:51.727561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.424047Z","title":"Fully convolutional networks for semantic segmentation,","venue":null,"work_id":"02387ac4-fe5b-4274-8187-9cc098bfee36","year":2015},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.805789Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:2f9103d230eeb6c8314ef2b50a1011b710679f5e90d6a0b32619ca6992ec4944","observation_id":"3c9760b2-6698-40dd-bacd-fa93414f981b","resolution":{"observed_at":"2026-08-06T17:02:06.428143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.411179Z","title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation,","venue":null,"work_id":"16a7d78f-b28a-4fa8-832e-ed7946bd7ac1","year":2017},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.883729Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:0e2aea94de1d11c5fad011f73030a2e8b3cb4a461a661a4ad625fc6ef24cac06","observation_id":"3f16cd62-21bb-40f3-b8dd-d3c9b5815b23","resolution":{"observed_at":"2026-08-06T17:02:06.415189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.397797Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,","venue":null,"work_id":"38fb6672-4638-47ee-a6d6-9e6027a5ac18","year":2017},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:51.966226Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:6f519210b6d506358a93878fc94bb0f10695aff735dc012deeb177a27e72c3ec","observation_id":"d06069d7-5521-42b2-ae63-e52f7fe9d953","resolution":{"observed_at":"2026-08-06T17:02:06.402067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-07T13:44:53.690521Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-06T17:01:52.033954Z","title":"Rethinking atrous convolution for semantic image segmentation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.033954Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:e2f8c730824d10a9a4ed22d0f9d8aab47d02c06a74c285237e3ba4b0b2de06cf","observation_id":"973da502-ec8b-4f9b-bfd4-91d7661cc248","resolution":{"observed_at":"2026-08-06T17:01:52.033954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.384295Z","title":"Encoder-decoder with atrous separable convolution for semantic im- age segmentation,","venue":null,"work_id":"d24568e9-ec67-44a2-8083-56f12558f05a","year":2018},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.110327Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:b5898b612452f251a55cf0cbb95c6ff6b5d1db3665968302c38a790c22b158b7","observation_id":"f0b9a866-b84e-401b-8235-5ab4e774e2f6","resolution":{"observed_at":"2026-08-06T17:02:06.388356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.371356Z","title":"Densely connected convolutional networks,","venue":null,"work_id":"345513e3-693d-4fad-a459-5c179bc8db4c","year":2017},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.169225Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:56084e0e53a57a8a519b4df8d66adc6542011b9f40a07dc412feafec805adfd6","observation_id":"7b9da585-d350-4486-9034-85e5e58570f8","resolution":{"observed_at":"2026-08-06T17:02:06.375587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.356633Z","title":"Deep high-resolution represen- tation learning for human pose estimation,","venue":null,"work_id":"5961c2d2-13df-43c6-859a-f0933cefc935","year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.229100Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:76fa8c013fd98c7f2480381d22ecf6dd2bb0ef10f590635344a760db71cd2d5f","observation_id":"2b7aef29-5d9c-4397-8514-02fdb1ce7c71","resolution":{"observed_at":"2026-08-06T17:02:06.361964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.340492Z","title":"Lite-hrnet: A lightweight high-resolution network,","venue":null,"work_id":"34d7d146-ad61-498d-96fa-7f754361358a","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.298175Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:68ee548378cbd95100863e705c55464b701922aeb36764ec26a70bf5d3b68154","observation_id":"4c7fafd2-f7ea-472a-980c-30ae75c46a82","resolution":{"observed_at":"2026-08-06T17:02:06.345664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.326233Z","title":"Segnext: Rethinking convolutional attention design for semantic segmentation,","venue":null,"work_id":"6eb49b8d-3262-45ae-9d27-b8e11541c383","year":null},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.362563Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:1ca1ffab7303d0c3c842bdb99e2d6b69992892c6293c12e17d6d4c0a2e6a6309","observation_id":"92191281-f22a-4285-a656-4538b69d5d27","resolution":{"observed_at":"2026-08-06T17:02:06.330529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.312365Z","title":"Segmenter: Trans- former for semantic segmentation,","venue":null,"work_id":"2d224d31-c7f0-4c75-ad1c-a2fdbe87f1f9","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.426094Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:6799887666933b58a1b07aa0dd9b9cf7633a1f91eecceae906399c466e2ff4ac","observation_id":"0e6db587-3113-429f-bb5f-426c39d047a1","resolution":{"observed_at":"2026-08-06T17:02:06.316578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.296908Z","title":"Multi-scale high-resolution vision transformer for semantic segmentation,","venue":null,"work_id":"30f634f9-1c25-48db-a232-581be110c85f","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.537673Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:4bdafc5d84717fa616b663f8d0ad269e63fd3dfd97b7f18807ae9472aae47fa0","observation_id":"a3b520c8-026b-4c31-b9a7-bdd68f7f8a61","resolution":{"observed_at":"2026-08-06T17:02:06.302030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.281173Z","title":"Gcnet: Non-local networks meet squeeze-excitation networks and beyond,","venue":null,"work_id":"13f299cc-25de-4168-b1f0-f39dca812b79","year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.623778Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:cd5684598a4bad5f899ade4c85c1d956f6457c0741498b99e1e230d61729ad1f","observation_id":"a412e995-9bed-4d37-b097-90bca1c77b03","resolution":{"observed_at":"2026-08-06T17:02:06.285921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.265278Z","title":"Ccnet: Criss-cross attention for semantic segmentation,","venue":null,"work_id":"73e67293-b85a-4df6-993a-b17ac75d6176","year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.700656Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:57b55b2482920da265d20b90903f38872e78d0c9eece4377fd68267003e834df","observation_id":"e77032cd-77ee-405b-84fb-116d55cb0885","resolution":{"observed_at":"2026-08-06T17:02:06.270665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.249792Z","title":"Pidnet: A real-time semantic segmentation network inspired by pid controllers,","venue":null,"work_id":"2523abfc-5754-427e-849b-2c9c36d74614","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.780355Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:7837f026711d5fbe5d5a9eaf0497e5c7c985346ad3a600a77892b56262a0edf2","observation_id":"8e1c2f54-6b38-4a67-aa11-b2b7dc5e949a","resolution":{"observed_at":"2026-08-06T17:02:06.254464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.234319Z","title":"Erfnet: Effi- cient residual factorized convnet for real-time semantic segmentation,","venue":null,"work_id":"702bff0b-c6ac-41b1-9bd5-5390cf9f1459","year":2017},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.865261Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:eb629142286f6956d1dfeef29d8b0d4d9065839f13e88768bc808617dd475069","observation_id":"37f5aef6-02df-43d2-9146-a532f4f409a1","resolution":{"observed_at":"2026-08-06T17:02:06.238473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.220447Z","title":"Mscfnet: a lightweight network with multi-scale context fusion for real-time semantic segmentation,","venue":null,"work_id":"41f96943-05d3-4d16-846f-205b6f44606c","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:52.978707Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:cb942713443a79e856fca4677f542e8d18d42c335bed5b8a3665d065c028a242","observation_id":"d74509a9-38f1-45bf-9011-8c0c6865deb1","resolution":{"observed_at":"2026-08-06T17:02:06.225027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.203922Z","title":"A multi-phase camera-lidar fusion network for 3d semantic segmentation with weak supervision,","venue":null,"work_id":"c065a213-c1f8-4ce4-8357-6df5b16d7562","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.089627Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:32b259532cd191183d1c370d67962ebb818599a0450003bbc81a7b5b88353c17","observation_id":"57f0ddc6-18af-4283-94b8-5f89daad83ef","resolution":{"observed_at":"2026-08-06T17:02:06.210955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.188726Z","title":"Rgb-d semantic segmentation and label-oriented voxelgrid fusion for accurate 3d semantic mapping,","venue":null,"work_id":"421c4508-de48-4e27-9e9e-2480aa22160e","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.170052Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:76d1923380cf45b31a9fdd1614b777391174b969d1ddaeb09a0af0fd98b042e7","observation_id":"0b11a6f9-c2a5-4c2d-b898-049ec0597909","resolution":{"observed_at":"2026-08-06T17:02:06.193358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.173102Z","title":"Confidence-and-refinement adaptation model for cross-domain seman- tic segmentation,","venue":null,"work_id":"6901649e-112c-42b8-884b-1b9eb20f9a09","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.251529Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:383d44fe56cf22d870fd57697b576467cc20ad626d00563c64b58d15e3ec0784","observation_id":"405f2185-ad8b-484b-8963-078fd0522754","resolution":{"observed_at":"2026-08-06T17:02:06.177461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.144525Z","title":"Learning texture invariant representation for domain adaptation of semantic segmentation,","venue":null,"work_id":"c3ff4976-5ab1-424c-9554-1ed601d6aaf1","year":2020},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.336731Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:3752d40ebecfc78f00ab9c40312900d8ed43ac9a03d15b17f523cbc59cc30f49","observation_id":"9fb4fa61-20d7-4b0b-98e2-fb9c7b62a4c8","resolution":{"observed_at":"2026-08-06T17:02:06.151643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.126547Z","title":"Affinity space adaptation for semantic segmentation across domains,","venue":null,"work_id":"9a423998-5f68-4df4-901e-073ae5bd3abe","year":2020},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.413319Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:6fa00c8d0651033317cca939e96ab37e12b1a0b6818fbcadf60cba490ddaf035","observation_id":"4f25c7d2-6d74-484e-9712-b16cc827359b","resolution":{"observed_at":"2026-08-06T17:02:06.131715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.107844Z","title":"Confidence regularized self-training,","venue":null,"work_id":"0580627e-1fc8-4e7c-955c-7ad78056c670","year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.498948Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:524a855bda155ff88ca4f1f74ea7ef011f26ce08d3e3a35ea3c5d278a7c44215","observation_id":"8dd5a659-c4cf-4040-9779-ffed52549cd0","resolution":{"observed_at":"2026-08-06T17:02:06.114647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.091828Z","title":"Rectifying pseudo label learning via uncer- tainty estimation for domain adaptive semantic segmentation,","venue":null,"work_id":"31da4168-e486-4b9a-b9d3-2670847b2690","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.615916Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:aa621c3d68a598954455728051e059fe448d77f1f5e3500b6d593c0b7d3ea17c","observation_id":"6eae430a-0989-4745-afda-0452bcd2c00e","resolution":{"observed_at":"2026-08-06T17:02:06.096139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.074622Z","title":"Towards robust semantic segmentation of accident scenes via multi- source mixed sampling and meta-learning,","venue":null,"work_id":"a7877bb5-c5c5-4601-bcd4-e0346af19859","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.688366Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:e28e5e1c0eed08ae66ccc0d1a67bde9ab50e02efc30a00fbd7294a645a3729c2","observation_id":"a57b6ef8-ade3-4c6e-98d6-cfebd1626c1b","resolution":{"observed_at":"2026-08-06T17:02:06.080380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.056218Z","title":"Dacs: Domain adaptation via cross-domain mixed sampling,","venue":null,"work_id":"41a2d692-a064-4332-a3bb-0957753eca66","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.789606Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:fcd1f320732229d57e98aa1d2a9518267864a0046c751bbd6bb349901be607d4","observation_id":"dddf3cab-f747-4909-b74d-c2ce3a0cc601","resolution":{"observed_at":"2026-08-06T17:02:06.063475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.038544Z","title":"Context-aware mixup for domain adaptive semantic segmentation,","venue":null,"work_id":"556ed8fd-dbdb-4e86-88b2-647ad10f3a94","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.872352Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:d219cf9654e56851e19a91af61ddbcd24047825c5a64026ebb0a020ad071f5a6","observation_id":"bd58a12c-75ca-484c-a8bb-d7eeacb3a5cf","resolution":{"observed_at":"2026-08-06T17:02:06.043957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.019251Z","title":"Daformer: Improving network architectures and training strategies for domain-adaptive semantic seg- mentation,","venue":null,"work_id":"5d9a2177-70a1-4686-b2b3-19a3d8bbfe79","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:53.971163Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:9225e002d40634898a7c93b1cfd16213ca7805b57e0876b4d918fc83f3c37b9e","observation_id":"e54fafad-91f5-4c89-be3d-3d604d2e7557","resolution":{"observed_at":"2026-08-06T17:02:06.025762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:06.003491Z","title":"Domain- invariant information aggregation for domain generalization semantic segmentation,","venue":null,"work_id":"23ecb959-bbd5-47fb-9869-42f6729b52e6","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.090280Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:1a19bc02bf4e2f496e7e1839f4b8e5c1d908de7e10349be3e1c37ae5940df316","observation_id":"7d6ad64d-4ca5-424b-9910-490e80174aa3","resolution":{"observed_at":"2026-08-06T17:02:06.008594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.986699Z","title":"Global and local texture randomization for synthetic-to-real semantic segmentation,","venue":null,"work_id":"a49a954e-2782-438d-b2dd-d582b26fba55","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.228639Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:13e58693a8b6bcece067bfea3e8fe0d8c7d91ec4a1760196cf84482c5d7ef297","observation_id":"7819b9ca-54ce-4c14-8a13-b35098e6c4d3","resolution":{"observed_at":"2026-08-06T17:02:05.991871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09068","last_updated":"2023-11-24T15:14:26Z","snapshot_observed_at":"2026-07-06T14:32:07.293340Z","submitted_at":"2022-12-18T11:42:51Z","title":"Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization","version":2},"cited_work":{"arxiv_id":"2212.09068","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.09068","snapshot_observed_at":"2026-08-06T17:01:59.006012Z","title":"Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization","venue":"cs.CV","work_id":"81071635-6aa6-4084-ad36-affcb4a82b36","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.307123Z"},"links":{"cited_paper":"/paper/2212.09068","citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:f194865627a76921634be1688e133c1fa89e6193eb244352e1fca5a921b49c92","observation_id":"2bb74a8f-5f08-46d5-b224-90e281ef2ef0","resolution":{"observed_at":"2026-08-06T17:01:59.055202Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.968600Z","title":"Two at once: Enhancing learning and generalization capacities via ibn-net,","venue":null,"work_id":"3b1be6b7-c6e8-409f-949d-847ee7203710","year":2018},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.428452Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:d1f2c8f1541a2cfa4ea60cdabc1f6096dfb82d3720a95c8a11f8d342e507e60a","observation_id":"8342595b-f3a3-4f15-886c-00ee2e23da21","resolution":{"observed_at":"2026-08-06T17:02:05.974124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.952302Z","title":"Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening,","venue":null,"work_id":"9b6a615c-0f0e-4537-bd0a-4102cdb87a52","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.545157Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:afdeb42d3779257fe4732be783371ef6b1ec96f9d7371565fccea2e374926388","observation_id":"76524e07-9b98-4529-a1b5-61816ee377ec","resolution":{"observed_at":"2026-08-06T17:02:05.958083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.935438Z","title":"Semantic-aware domain generalized segmentation,","venue":null,"work_id":"2d75508e-73d2-428e-87ed-ffbdca6132a4","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.616336Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:76003e22c5b12094ea31687f6b512c1646f05750ed5cb0aae12021230e13a82e","observation_id":"f92e7e1c-8eb3-4a9c-bcdf-b11856e119e9","resolution":{"observed_at":"2026-08-06T17:02:05.941523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.918428Z","title":"Generalizable model-agnostic se- mantic segmentation via target-specific normalization,","venue":null,"work_id":"a0b2e4e9-1c4f-4b83-81c4-f90d38f4aa23","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.694702Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:819f265d318531998af0b97d02e57336aa9a654fa07d252d68a89d1f82e16a27","observation_id":"a9eff0d3-7db6-42bc-8d0a-fccb5a1edfa5","resolution":{"observed_at":"2026-08-06T17:02:05.923971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.899346Z","title":"Pin the memory: Learning to generalize semantic segmentation,","venue":null,"work_id":"71f833be-57c6-4dcf-9d3d-398591fbe982","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.785799Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:1a8b370f4c5ab9b43307ef6cdf7f6c3c5df3a6f441a8cc13530140c056cb7c8c","observation_id":"96bac6ac-3174-4b4f-b8b8-9a9e953dcd08","resolution":{"observed_at":"2026-08-06T17:02:05.908897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.714626Z","title":"Fine- grained self-supervision for generalizable semantic segmentation,","venue":null,"work_id":"65250636-0ea1-42d1-9492-771c027bae95","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:54.881239Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:8fd126f7451f1bc9135dfd5f36d40e6d6ceefad91dc48e8483c3f8f093b0862d","observation_id":"6c019888-c287-41bd-9c20-37b595d5e56d","resolution":{"observed_at":"2026-08-06T17:02:05.832547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.587566Z","title":"Class-balanced sampling and discriminative stylization for domain generalization se- mantic segmentation,","venue":null,"work_id":"cb1fbcec-6e1f-4d64-b504-80b3cf48a0ab","year":2024},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.035477Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:4775e33bd969269d437eff85c7de4281d764a789875583e640d3caffafb788b6","observation_id":"278388d0-ea39-49e1-b52b-f2fae8d0cd03","resolution":{"observed_at":"2026-08-06T17:02:05.637196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.445781Z","title":"Calibration- based multi-prototype contrastive learning for domain generalization semantic segmentation in traffic scenes,","venue":null,"work_id":"6ea8aaa0-a5bd-408a-8f6b-124eb3ccfa5e","year":2024},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.103519Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:b6fe16ac21b50ffbfbd6c480bd794df6100ee72be9f44641d26f14e0c0a6b3ba","observation_id":"5c869747-6e2d-4f14-943b-008447489bbd","resolution":{"observed_at":"2026-08-06T17:02:05.501965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.414595Z","title":"Cris: Clip-driven referring image segmentation,","venue":null,"work_id":"3e9bfc59-9397-4659-a4c6-0df9b4f09e12","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.184303Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:5a86c9f0ee4bdc82259fcf8117bbccb3ebec7fe4db4ac427a6e38562f75f9562","observation_id":"8eb73aa1-97ae-4610-8f6a-b0cab83bda1b","resolution":{"observed_at":"2026-08-06T17:02:05.431959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:05.220116Z","title":"Referring image segmentation using text supervision,","venue":null,"work_id":"7af166a3-7691-4300-8aa6-f3c5d2eae836","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.301185Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:a99efa0655a71a1674829d9f43c428a9fda2b17bb4dc0f3278dfd3b80a78c885","observation_id":"1ff28e1f-edf3-498f-bf6a-d76a0693918b","resolution":{"observed_at":"2026-08-06T17:02:05.373359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:04.987684Z","title":"Unsupervised domain adaptation for referring semantic segmentation,","venue":null,"work_id":"38c365d9-2882-4864-893c-46df38597c40","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.376163Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:866d9274610e2c0eea6afb36b9e435af7dc6bf4cb820e55532ab04b61e0048c5","observation_id":"d4a8c134-490d-4a52-88f0-98e037ffb6d1","resolution":{"observed_at":"2026-08-06T17:02:05.065532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:04.974047Z","title":"A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model,","venue":null,"work_id":"2ceb276d-82bb-4cac-b440-9381f2aefda0","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.518151Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:4e39a0b3102552a183c42adbf20934c122436a0ba1d3b077f8276c29303152ed","observation_id":"81329301-0237-4cbe-97b0-89f677d95f5c","resolution":{"observed_at":"2026-08-06T17:02:04.978678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:04.802812Z","title":"Groupvit: Semantic segmentation emerges from text supervision,","venue":null,"work_id":"bc4f6269-bc21-4cbe-bcdb-3cc194d341d6","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.610363Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:eab170b977d818a75de9000ed69784b8d367c2f75c649abff2711579a2c009eb","observation_id":"4a2541fb-573f-42a1-9652-461060f6c699","resolution":{"observed_at":"2026-08-06T17:02:04.902482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:04.695672Z","title":"Open-world semantic segmentation via contrasting and clustering vision-language JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 16 embedding,","venue":null,"work_id":"00c3d944-08cc-46c3-af29-f31db969436f","year":2021},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.697833Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:4906487475b9b8675b9c0a0b9a7eeedee616f1f47055142406d1c794d98504fb","observation_id":"2404deba-2a7a-4b21-9719-2bd8b47f5f92","resolution":{"observed_at":"2026-08-06T17:02:04.752639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:04.600436Z","title":"Decouplenet: Decoupled network for domain adaptive semantic segmentation,","venue":null,"work_id":"69b9c9e5-c43e-4338-950a-e54a0be30fa9","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.795557Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:0007649f6bdbb19b6d45218eaee9b7be6c2918236947cbc697d3b9345a58e290","observation_id":"bf9f9489-f473-4c78-baa6-9c099b7bec44","resolution":{"observed_at":"2026-08-06T17:02:04.633890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:04.398298Z","title":"Subsidiary prototype alignment for universal domain adaptation,","venue":null,"work_id":"64a03966-e870-4090-8271-1656ede1fb73","year":null},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.911242Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:738f788106dd1602333e12790cd1ea4c575878c8007243ebd967c1b188a4d9c8","observation_id":"98b3b7a4-7c22-4186-8396-c9432e551645","resolution":{"observed_at":"2026-08-06T17:02:04.475494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:04.203351Z","title":"Adaptive refining-aggregation-separation framework for unsupervised domain adaptation semantic segmentation,","venue":null,"work_id":"e9370b81-42c6-422f-9398-3e35e7257d9d","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:55.997100Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:50b3ecabed17e4d7cbd8be832ca2014061a160afbb36df6acde136c066aee398","observation_id":"da52d4be-455e-42a0-89ce-f040291333a4","resolution":{"observed_at":"2026-08-06T17:02:04.281263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:03.969819Z","title":"Performance evaluation of texture measures with classification based on kullback discrimination of distributions,","venue":null,"work_id":"1ee315ea-3693-4911-95bf-ef02a01e124b","year":1994},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.064228Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:713882e976cd882aafcbff8e0cf18c7828dedaff56bc9e5b3102964da36fe2fc","observation_id":"61dc2f96-1d2f-498c-b5f8-1a7ea1b00a1c","resolution":{"observed_at":"2026-08-06T17:02:04.078360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:03.723223Z","title":"Pietik ¨ainen, A","venue":null,"work_id":"86411cdc-6542-4a9b-b690-1e59abb6603f","year":2011},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.150988Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:0766137da5f0d31106fcabe582c47c5e25aa65c64f8035f79dad3bbb9ca0fe39","observation_id":"8f973cac-6616-41bb-b797-8c43e6eb56a4","resolution":{"observed_at":"2026-08-06T17:02:03.841632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:03.483821Z","title":"A global reweighting approach for cross-domain semantic segmentation,","venue":null,"work_id":"38d2292f-9aa7-44cd-8324-b6d736307e0a","year":2024},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.279789Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:e0bc834835db045e39d10c866a90e57846c9c89be0a88bd7d65752aeaf625ada","observation_id":"df7b26d6-162d-4b54-8db8-d2c1ed383d74","resolution":{"observed_at":"2026-08-06T17:02:03.606887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:03.277467Z","title":"A theory of learning from different domains,","venue":null,"work_id":"4d3c8566-15b6-4f6f-82d8-48fb7510e0f0","year":2010},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.391082Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:93762394d3bd552ad8bb815200cbe35281e2f020304f9befba9c2c46cf5d41ea","observation_id":"717910c0-a0a6-4058-9756-7b8918c70a6f","resolution":{"observed_at":"2026-08-06T17:02:03.394821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00804","last_updated":"2021-09-15T14:17:56Z","snapshot_observed_at":"2026-08-10T04:57:15.839349Z","submitted_at":"2019-11-03T01:03:15Z","title":"Generalizing to unseen domains via distribution matching","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00804","snapshot_observed_at":"2026-08-06T17:01:56.481709Z","title":"Generalizing to unseen domains via distribution matching,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.481709Z"},"links":{"cited_paper":"/paper/1911.00804","citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:5b147f7b8bf7e77740b86d290d6413a5eaeafdb163f31f57a174bbf003f6c0a8","observation_id":"29f2970d-f96b-4148-9748-b0b04a4f6905","resolution":{"observed_at":"2026-08-06T17:01:56.481709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:02.948214Z","title":"Aadg: automatic augmentation for domain generalization on retinal image segmentation,","venue":null,"work_id":"5acf9a3d-da5c-4f30-91b8-50a11bf9e818","year":2022},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.576680Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:b357950b13b284fe57c917608fb70a49b429b71f4fbde6b93ac63584700d6eff","observation_id":"f1df5c99-8237-45b2-af17-f70aa8432b39","resolution":{"observed_at":"2026-08-06T17:02:03.127021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:02.589092Z","title":"Learning shape-invariant representation for generalizable semantic segmenta- tion,","venue":null,"work_id":"24d4d55b-c038-4757-8420-4f9953d9471f","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.665657Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:4969492de10de6decc54df66565046da78cae2508ac51d66f130ad1313b14759","observation_id":"ea4bb37e-922e-4109-8f85-4278aee53eb5","resolution":{"observed_at":"2026-08-06T17:02:02.716657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:02.575756Z","title":"Video generalized semantic segmentation via non-salient feature rea- soning and consistency,","venue":null,"work_id":"b2a33523-a4bd-4d1e-9028-362f83237720","year":2024},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.758648Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:5aa1b13252175ba5dfaa1be9e871b0fb00bcd04ee5ddacb29c4196ef593d0828","observation_id":"2f79bfa8-4d01-409f-b51d-6aefc557e3a5","resolution":{"observed_at":"2026-08-06T17:02:02.579732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:02.440857Z","title":"Towards robust object detection invariant to real-world domain shifts,","venue":null,"work_id":"54fc0433-7514-41f1-b932-e92a89eaab44","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.868572Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:bf1dc52fa73993b9a9d412868b03285ccdba7a050a1ab0f4f1843ea8db352c9b","observation_id":"814d1f01-ce5a-4bb2-b6d4-0ea04187cf86","resolution":{"observed_at":"2026-08-06T17:02:02.490586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:02.315818Z","title":"Progres- sive random convolutions for single domain generalization,","venue":null,"work_id":"c9d02241-94d5-4508-a974-4727ff8684d9","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.919822Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:61fd6facbabda75c9d8f82757b41949c45b52b8c1132500d2d14d89a88b65890","observation_id":"2f95eeb5-9bf6-4938-bf5c-5cfdfbacd6f8","resolution":{"observed_at":"2026-08-06T17:02:02.368321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:02.198095Z","title":"An information-theoretic method to automatic shortcut avoidance and domain generalization for dense prediction tasks,","venue":null,"work_id":"80415cf2-6547-4f7c-ae79-01aba6dcc0b2","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.984435Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:e24a336d265503b887876b2d2ecee42de9ff6bd877cf2193e7eb688d990ee084","observation_id":"ec34b16e-109d-4060-bab8-2cebe2801752","resolution":{"observed_at":"2026-08-06T17:02:02.254793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:02.059631Z","title":"Order-preserving consistency regularization for domain adaptation and generalization,","venue":null,"work_id":"d8842abe-e4af-4cf4-babc-2aae42a4810f","year":2023},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:57.063669Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:d059f2860dda5a30b4cc739146ee870d95675cdfe7eaa6e1e98527c0e12da730","observation_id":"5db93660-f9f7-423f-b780-b02391bd3732","resolution":{"observed_at":"2026-08-06T17:02:02.142513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:01.939847Z","title":"The cityscapes dataset for semantic urban scene understanding,","venue":null,"work_id":"7839f5af-3271-458e-afa4-b4da20997ace","year":2016},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:57.187947Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:c749cb9c97c4025aeac26ec47cf3670af68e5404138585011fa5718aa1348fea","observation_id":"4f4a0575-3286-4a41-8dcb-5032954baf11","resolution":{"observed_at":"2026-08-06T17:02:01.998730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:01.803947Z","title":"Bdd100k: A diverse driving dataset for heterogeneous multitask learning,","venue":null,"work_id":"80ca9e5e-ad53-4e50-a9e1-9209582bbf23","year":2020},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:57.281351Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:57ff8073537660ee316a9ccb70b071dfed6336934b2597ffad5e6ed3d0c4b500","observation_id":"be66b6f4-d1d4-4bd2-8107-55c937a1cc38","resolution":{"observed_at":"2026-08-06T17:02:01.867415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:01.680304Z","title":"The mapillary vistas dataset for semantic understanding of street scenes,","venue":null,"work_id":"75a6531c-0de4-46a0-8b5c-ff776cded0ab","year":2017},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:57.394596Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:e9209b64fc36ec5f4209d286fc39042bced4fee5bc8a088ee184ae38b5b27448","observation_id":"c093d1f1-8a70-490f-a778-4cb14fe42142","resolution":{"observed_at":"2026-08-06T17:02:01.737045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:01.523020Z","title":"Idd: A dataset for exploring problems of autonomous navigation in unconstrained environments,","venue":null,"work_id":"3be2f166-3800-4aa6-aed1-f723dbd5da1a","year":2019},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:57.479178Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:0b0e7e163182c1c8d703ca38022982b7a4e55209fe0584ad2d0a427e4aa05096","observation_id":"3dda165c-3f54-498c-b353-1e64c66fa0a8","resolution":{"observed_at":"2026-08-06T17:02:01.606568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:01.376013Z","title":"The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,","venue":null,"work_id":"5decba5e-2778-45ee-997e-94a28e002b7f","year":2016},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:57.573887Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:8cb4ad3acb2948ebf8f14591a93a34f519fbc3630c4ef413a5f093170dec2c0f","observation_id":"ebabd0f6-ec90-4ddb-8e0c-52cc544a8985","resolution":{"observed_at":"2026-08-06T17:02:01.444466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:02:01.198685Z","title":"Playing for data: Ground truth from computer games,","venue":null,"work_id":"34ad0e63-bf2a-47ff-94d2-e1c45b1273c8","year":2016},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:57.647158Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:3355d0be30f2c178f6fb88f0359ebf747fea9b3760f1ca5be602724200563b31","observation_id":"53bf48a8-359c-4e9f-bee9-3eef4ce9596e","resolution":{"observed_at":"2026-08-06T17:02:01.274256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:01:57.729474Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:57.729474Z"},"links":{"citing_paper":"/paper/2507.11955"},"observation_digest":"sha256:c0bb5190584bf450f40e42ee5ff815da9f9fe2aadd96da904f67e67669a04367","observation_id":"604bd70b-193d-47e8-a59b-707557518001","resolution":{"observed_at":"2026-08-06T17:01:57.729474Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.11955","last_updated":"2025-07-16T06:42:21Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T04:57:36.117110Z","submitted_at":"2025-07-16T06:42:21Z","title":"Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":37,"verified_exact":1,"verified_fuzzy":62},"total_outbound_references":114},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 100 of 114 outbound references and 0 inbound Pith citation observations for arXiv:2507.11955."}