{"as_of":"2026-08-17T21:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bda72ee64f1781913a32208a6b574a075163488bc5da38249782f9032a2fa8ce","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T10:43:46.146585Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":23,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:02:05.009084Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T13:49:51.655649Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2404.12390","last_updated":"2024-07-03T08:44:45Z","snapshot_observed_at":"2026-08-14T00:50:08.075021Z","submitted_at":"2024-04-18T17:59:54Z","title":"BLINK: Multimodal Large Language Models Can See but Not Perceive","version":4},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-15T20:18:15.439163Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2404.12390"},"observation_digest":"sha256:b03d6c0fe4c8d3ede0d99a4b150cc42f15e92016004532d15af143905309366e","observation_id":"f49f3f48-d0ac-4389-8315-31b5ccd71c56","resolution":{"observed_at":"2026-05-15T20:18:15.503362Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-08-12T00:59:25.267762Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2412.01827","last_updated":"2025-07-08T00:51:16Z","snapshot_observed_at":"2026-08-12T16:02:34.405350Z","submitted_at":"2024-12-02T18:59:53Z","title":"RandAR: Decoder-only Autoregressive Visual Generation in Random Orders","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T00:59:25.267762Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2412.01827"},"observation_digest":"sha256:dc92ac97b0b9f30eb1cb05174e5c8db3b860aeba5407c4604cefe7644979b1a2","observation_id":"d36eb7f2-ffe0-4996-8fb6-7b2e380ed42a","resolution":{"observed_at":"2026-08-12T00:59:25.267762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-08-11T22:24:44.167314Z","title":"Spair-71k: A large-scale benchmark for semantic corre- spondence","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2412.03512","last_updated":"2024-12-04T17:55:33Z","snapshot_observed_at":"2026-08-14T00:35:53.437193Z","submitted_at":"2024-12-04T17:55:33Z","title":"Distillation of Diffusion Features for Semantic Correspondence","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T22:24:44.167314Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2412.03512"},"observation_digest":"sha256:31647a9736e2a5004db3e9b680589de20f411bd32620a3a7b0559a9dbe20bfd3","observation_id":"c501aa75-1cee-4245-b38c-442ba2024efd","resolution":{"observed_at":"2026-08-11T22:24:44.167314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-08-10T14:51:27.274225Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2501.14945","last_updated":"2025-01-24T22:10:45Z","snapshot_observed_at":"2026-08-15T17:23:16.344452Z","submitted_at":"2025-01-24T22:10:45Z","title":"MATCHA:Towards Matching Anything","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T14:51:27.274225Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2501.14945"},"observation_digest":"sha256:078d9bf8c33a3dba114c4dceb3bcafb7ca802721adf86f0ca4e8a8f5839719a8","observation_id":"a4577834-f953-4781-ab6a-9e788719e063","resolution":{"observed_at":"2026-08-10T14:51:27.274225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2503.17715","last_updated":"2026-05-05T11:28:12Z","snapshot_observed_at":"2026-08-17T21:08:58.100174Z","submitted_at":"2025-03-22T10:09:11Z","title":"Normalized Matching Transformer","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-22T22:03:16.161201Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2503.17715"},"observation_digest":"sha256:7ff7d707219816d3e931d0affd5eb48d9b24e9216e6dacf88ea5abdfa454f3f8","observation_id":"c995a763-229b-4fe7-9a5d-bbd5e1758292","resolution":{"observed_at":"2026-05-22T22:05:11.585822Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-08-16T05:02:05.009084Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2504.21749","last_updated":"2025-04-30T15:42:23Z","snapshot_observed_at":"2026-08-17T03:05:03.944676Z","submitted_at":"2025-04-30T15:42:23Z","title":"Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T05:02:05.009084Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2504.21749"},"observation_digest":"sha256:46afec5c4207f5705313bc46d3f52a4efc06abd76bff15594c760c524f37f2c7","observation_id":"4e51573d-16e6-414c-8ef2-86cf01883539","resolution":{"observed_at":"2026-08-16T05:02:05.009084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-08-07T14:38:30.416148Z","title":"SPair-71k: A large-scale bench- mark for semantic correspondence,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2505.18060","last_updated":"2026-06-28T05:51:41Z","snapshot_observed_at":"2026-08-15T14:36:48.064734Z","submitted_at":"2025-05-23T16:07:16Z","title":"Semantic Correspondence: Unified Benchmarking and a Strong Baseline","version":4},"reference_index":122,"source":"pdf_text","source_observed_at":"2026-08-07T14:38:30.416148Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2505.18060"},"observation_digest":"sha256:485ef12439d2bb7ad85ae4b898e383be76846621ea5a28d48f7de041b6110dbf","observation_id":"04c66538-d0f8-4ee3-bbf4-8bf3e63d5143","resolution":{"observed_at":"2026-08-07T14:38:30.416148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-08-07T05:25:31.919369Z","title":"Spair-71k: A large-scale benchmark for semantic correspondence, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.08008","last_updated":"2025-06-09T17:59:54Z","snapshot_observed_at":"2026-08-15T07:02:37.929930Z","submitted_at":"2025-06-09T17:59:54Z","title":"Hidden in plain sight: VLMs overlook their visual representations","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T05:25:31.919369Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2506.08008"},"observation_digest":"sha256:f75e195800c299c23006f143a015365079160c121b6a76c0be1fbf677f63f70f","observation_id":"b9114a3f-163d-495a-a67c-fd0091df70d4","resolution":{"observed_at":"2026-08-07T05:25:31.919369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2507.14137","last_updated":"2026-04-25T14:06:10Z","snapshot_observed_at":"2026-07-06T21:59:25.077202Z","submitted_at":"2025-07-18T17:59:55Z","title":"Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning","version":4},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-05-19T03:39:52.969100Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2507.14137"},"observation_digest":"sha256:fc6957659788f65c3e7a234ed84c580b21d474cf146b8af5e3f91d94e8be030e","observation_id":"fd8daf60-fa7a-41fc-9f08-1699fe808bc7","resolution":{"observed_at":"2026-05-19T03:42:01.369687Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-08-06T10:16:53.667014Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.00272","last_updated":"2025-08-01T02:38:39Z","snapshot_observed_at":"2026-08-15T11:58:25.178134Z","submitted_at":"2025-08-01T02:38:39Z","title":"Towards Robust Semantic Correspondence: A Benchmark and Insights","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T10:16:53.667014Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2508.00272"},"observation_digest":"sha256:ec29e6fbe8b7b95d348c43bf299cd8a624a87ba08f3be2fc1ad3039d28cfe5ab","observation_id":"2596333c-3ad1-4146-a39c-97711e9085a5","resolution":{"observed_at":"2026-08-06T10:16:53.667014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-08-05T10:37:52.529822Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2509.03893","last_updated":"2025-09-04T05:39:16Z","snapshot_observed_at":"2026-08-12T19:08:47.124978Z","submitted_at":"2025-09-04T05:39:16Z","title":"Weakly-Supervised Learning of Dense Functional Correspondences","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T10:37:52.529822Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2509.03893"},"observation_digest":"sha256:0b99460d10b5f81d34d0fa33026f5f043333569183017be2cded03d0f866f554","observation_id":"f0354f1f-47b7-4142-bfd0-501b48ffd234","resolution":{"observed_at":"2026-08-05T10:37:52.529822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2604.02486","last_updated":"2026-08-04T17:24:30Z","snapshot_observed_at":"2026-08-15T16:43:34.966592Z","submitted_at":"2026-04-02T19:40:56Z","title":"VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-13T21:56:29.924506Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2604.02486"},"observation_digest":"sha256:2985fa36b704d7d1d3eb8730b17b46a0aa3cf75cb8a64acd142f5dd9db4f283a","observation_id":"7b4c1eec-7a33-4f31-9091-beeecad501a6","resolution":{"observed_at":"2026-05-13T21:58:19.856641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2604.14433","last_updated":"2026-04-15T21:24:42Z","snapshot_observed_at":"2026-08-15T04:56:39.475831Z","submitted_at":"2026-04-15T21:24:42Z","title":"Zero-Ablation Overstates Register Content Dependence in DINO Vision Transformers","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T12:56:32.421877Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2604.14433"},"observation_digest":"sha256:13ad0861fbca2f095946c5edbfe1cf0dbb93a942da56f82fcaadef93c6f021c2","observation_id":"653853b2-a1ab-459f-955b-f1bb230fca5c","resolution":{"observed_at":"2026-05-10T13:00:24.713018Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2604.18267","last_updated":"2026-04-20T13:44:46Z","snapshot_observed_at":"2026-08-11T18:51:48.820723Z","submitted_at":"2026-04-20T13:44:46Z","title":"MARCO: Navigating the Unseen Space of Semantic Correspondence","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-10T05:44:07.771467Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2604.18267"},"observation_digest":"sha256:72d25f35d101b77f09ab5326bf46bc2f696b5c85334ad6b4a242112051a6787b","observation_id":"fa34e3b8-efe9-479f-9148-5ea79085e0ca","resolution":{"observed_at":"2026-05-10T05:46:10.305081Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2605.17472","last_updated":"2026-05-20T04:42:46Z","snapshot_observed_at":"2026-08-16T13:44:22.835716Z","submitted_at":"2026-05-17T14:20:53Z","title":"Weighted Reverse Convolution for Feature Upsampling","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-20T14:24:28.728963Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2605.17472"},"observation_digest":"sha256:1fedb6c0598719e36c46c12ab70431e5369cbe9ea4d24b41bc842ec615c19417","observation_id":"3811c435-1931-4f64-8aeb-e33f1b95e7db","resolution":{"observed_at":"2026-05-20T14:28:21.690132Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2605.17472","last_updated":"2026-05-20T04:42:46Z","snapshot_observed_at":"2026-08-16T13:44:22.835716Z","submitted_at":"2026-05-17T14:20:53Z","title":"Weighted Reverse Convolution for Feature Upsampling","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-21T08:15:52.610554Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2605.17472"},"observation_digest":"sha256:f5095c928b664992caf01988fbd50c62fd56e73bbf6d228071a0149dcf2b5a3a","observation_id":"b502ba0d-b1f7-4bb1-9e9a-266280335d92","resolution":{"observed_at":"2026-05-21T08:19:52.821986Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2605.28257","last_updated":"2026-05-27T10:08:32Z","snapshot_observed_at":"2026-08-16T11:48:41.689327Z","submitted_at":"2026-05-27T10:08:32Z","title":"Category-Level 3D Correspondence in Camera Space via Morphable Object Priors","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T13:56:46.017507Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2605.28257"},"observation_digest":"sha256:c67708267a379796adcc117f2ad08b9fdfc195b9b3df8a751eadda14879a4160","observation_id":"11661d3a-42bd-4f0f-a8c4-a9353002546b","resolution":{"observed_at":"2026-06-29T14:03:29.628684Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2605.30093","last_updated":"2026-05-28T15:37:59Z","snapshot_observed_at":"2026-08-15T01:10:29.242026Z","submitted_at":"2026-05-28T15:37:59Z","title":"Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T08:27:20.516403Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2605.30093"},"observation_digest":"sha256:b5b2529bda1b8f45cb16970ca85f085fe45b09048f79e92206ad449a0db2a443","observation_id":"1884f1d1-2dbe-4757-a043-2e818671c5cd","resolution":{"observed_at":"2026-06-29T08:33:15.448516Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2605.31597","last_updated":"2026-07-01T10:11:16Z","snapshot_observed_at":"2026-08-12T16:30:43.319268Z","submitted_at":"2026-05-29T17:58:48Z","title":"SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-28T23:02:50.100304Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2605.31597"},"observation_digest":"sha256:2b560200e76b9195753836ec0fee459ef195652d2dbccea129b164790b8971c0","observation_id":"afea8e27-d6ab-4ae2-83f7-0c0aeede44f2","resolution":{"observed_at":"2026-07-01T19:16:00.314908Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2605.31597","last_updated":"2026-07-01T10:11:16Z","snapshot_observed_at":"2026-08-12T16:30:43.319268Z","submitted_at":"2026-05-29T17:58:48Z","title":"SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-02T22:50:50.041229Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2605.31597"},"observation_digest":"sha256:53bd58994402c8bd1a0db3ad108f8889544410e4d2419da58f7a7c9d37631866","observation_id":"f597beff-f7a8-43e2-b405-35d8740e1966","resolution":{"observed_at":"2026-07-02T22:57:25.738689Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":"1908.10543","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-04T13:49:51.655649Z","title":"Spair-71k: A large-scale benchmark for semantic correspon- dence","venue":null,"work_id":"5204ff4e-af27-47db-8f90-6af4daf72cf3","year":1908},"citing_paper":{"arxiv_id":"2606.27354","last_updated":"2026-06-25T17:56:27Z","snapshot_observed_at":"2026-08-14T14:33:35.092857Z","submitted_at":"2026-06-25T17:56:27Z","title":"Error-Conditioned Neural Solvers","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-06-26T04:54:56.698035Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2606.27354"},"observation_digest":"sha256:d602f746afd3e80f18a0fb01f709650417fc45eff783336fdf9a92672be27111","observation_id":"9f1c42d2-901b-4567-914d-f25da07d8332","resolution":{"observed_at":"2026-07-04T13:49:51.657262Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-12T05:46:45.293902Z","title":"Spair-71k: A large-scale benchmark for semantic correspondence,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2607.02968","last_updated":"2026-07-03T05:21:46Z","snapshot_observed_at":"2026-08-16T15:16:10.440947Z","submitted_at":"2026-07-03T05:21:46Z","title":"Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-12T05:46:45.293902Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2607.02968"},"observation_digest":"sha256:1f7d2891183a491ebd9336c3f324326e0d5a53719d639c1c425c3f679037fa21","observation_id":"9799326c-c865-4fc0-8f26-db82555e0d30","resolution":{"observed_at":"2026-07-12T05:46:45.293902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10543","snapshot_observed_at":"2026-07-11T10:12:56.715647Z","title":"arXiv prepreint arXiv:1908.10543 (2019)","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2607.05006","last_updated":"2026-07-06T12:47:03Z","snapshot_observed_at":"2026-08-13T23:10:44.400829Z","submitted_at":"2026-07-06T12:47:03Z","title":"Unsupervised Pixel-Level Semantic Left-Right Understanding of In-the-Wild Images","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-11T10:12:56.715647Z"},"links":{"cited_paper":"/paper/1908.10543","citing_paper":"/paper/2607.05006"},"observation_digest":"sha256:5e3864496fa797e0fe9370b71f29026ced81308fd4f418f8e82fc66e3d4a68b6","observation_id":"aa287d58-d785-4a36-ad50-36fca4b80ed3","resolution":{"observed_at":"2026-07-11T10:12:56.715647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1908.10543/citation-record","integrity":"/paper/1908.10543/integrity","json":"/paper/1908.10543/citation-record.json","paper":"/paper/1908.10543"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T10:43:46.338960Z","title":"Detect what you can: De- tecting and representing objects using holistic models and body parts","venue":null,"work_id":"e02eb072-2d32-4ae0-bd78-4a10b0f14d02","year":2014},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.091406Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:6918717785a716313c4fd5e53c4fdc11bf4fd8c8f7eac4361cc830bdca314001","observation_id":"fb33d42f-7f9c-42f8-96b6-a019755cbafe","resolution":{"observed_at":"2026-08-14T10:43:46.343074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.326791Z","title":null,"venue":null,"work_id":"23ecf808-f52b-4bdb-bcf1-6b398b08a361","year":2015},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.095997Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:8c69e89cbecfdcf7d3ae5bffedfcd5b972deffeb55e9f1c93f57d4285ec41684","observation_id":"458dab13-e56c-4138-877c-75b379121a28","resolution":{"observed_at":"2026-08-14T10:43:46.331307Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.314432Z","title":"Proposal ﬂow","venue":null,"work_id":"c2a9274d-bc82-408f-a822-e8b19ae76576","year":2016},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.100221Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:01ff662921c1769a8b7e7ba5d1b8afe7a964c5cb6d1b3bb8f05161fb44b88f32","observation_id":"7ea65b1d-01ac-47df-bf4b-04051380ed9f","resolution":{"observed_at":"2026-08-14T10:43:46.318302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.302829Z","title":"Proposal ﬂow: Semantic correspondences from ob- ject proposals","venue":null,"work_id":"62bebf53-94e1-4436-b230-df6319102a66","year":2018},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.104196Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:68dd994685f71b6900c757ba299cc715987f8981c13ef40f79f9c231989d23e4","observation_id":"95450105-90a9-483c-b7c2-35086aba05f0","resolution":{"observed_at":"2026-08-14T10:43:46.307151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.290576Z","title":"Scnet: Learning semantic correspondence","venue":null,"work_id":"7321b773-9e0c-4e0f-9da1-2fa044c78139","year":2017},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.108335Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:c25177d25f746e7491161f781df0fb66a60f6f2a3e6e01b27aa1381886efd9cc","observation_id":"779955b7-14ff-4def-bc57-e989500234a8","resolution":{"observed_at":"2026-08-14T10:43:46.294414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.278435Z","title":"De- formable spatial pyramid matching for fast dense correspon- dences","venue":null,"work_id":"c042d7ad-31a3-45d8-82f5-48c47a777c76","year":2013},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.112364Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:71253b6ef4df876afa4842fba9797822d4a09f67855be05fe7a6e4a34fa425f3","observation_id":"eb2e1732-fdce-4d19-ae8b-8ec09fff5c8f","resolution":{"observed_at":"2026-08-14T10:43:46.282642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.265971Z","title":"Sift ﬂow: Dense correspondence across scenes and its applications","venue":null,"work_id":"4a0fa566-5f50-4ddb-b6ad-c592409e57b6","year":2008},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.117001Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:ec11607b85903b39294a3ca615e80cedbdb4a77582b009d6a82668c1402a68a9","observation_id":"d3b2b946-b41b-43d2-b442-00a18b134614","resolution":{"observed_at":"2026-08-14T10:43:46.270167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.253470Z","title":"Hyperpixel ﬂow: Semantic correspondence with multi-layer neural features","venue":null,"work_id":"7c63bc83-5f0b-4688-9014-e6374e5a0d5a","year":2019},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.120983Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:8d7469514f8d3285c2b2b633953a9e776733bb7fa03daefd7a768540705f7d3c","observation_id":"18c3a992-102b-4812-acaf-9cce859caa58","resolution":{"observed_at":"2026-08-14T10:43:46.257771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.241097Z","title":"Convo- lutional neural network architecture for geometric matching","venue":null,"work_id":"e4f2b824-01e2-4100-a1e2-5860276422e1","year":2017},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.125158Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:5a327af4d615ac8507890ecab28ef007f36b868a5f31bf9bc83e8dfd8b7d75a2","observation_id":"7eb73ced-d9d3-45ee-998d-7e95f14ef06c","resolution":{"observed_at":"2026-08-14T10:43:46.245311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.227745Z","title":"End-to- end weakly-supervised semantic alignment","venue":null,"work_id":"e2e8acc3-8883-4338-a6ef-ab405311fc52","year":2018},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.129353Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:78b46cfc72d9f64a7e1089bd21a46eba20f7dde07131478052271222d1e59575","observation_id":"0ac1d001-c1f1-4dd0-a4fa-dd163e1b1ba0","resolution":{"observed_at":"2026-08-14T10:43:46.232192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.214601Z","title":"Neighbourhood con- sensus networks","venue":null,"work_id":"2024ad14-52bb-4341-a6fe-2b1b10da0c21","year":2018},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.134278Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:c1353c3891edabeb778dddd671be457315f931ce8b2a8d30be3273fb0f59c06d","observation_id":"adeebe84-e5c1-4b29-8e63-e5707b8cefe3","resolution":{"observed_at":"2026-08-14T10:43:46.218715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.202508Z","title":"Attentive semantic alignment with offset-aware correlation kernels","venue":null,"work_id":"bdcb6d7c-69ff-4395-be86-9e191598bacd","year":2018},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.139075Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:4f7b45874911ebee9b48b513619c46ba69742bed14f43b0aebba52eba5ea93e6","observation_id":"2ee4f8a4-d8f8-419b-8667-1135c53588dc","resolution":{"observed_at":"2026-08-14T10:43:46.206558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.187984Z","title":"Joint re- covery of dense correspondence and cosegmentation in two images","venue":null,"work_id":"34745918-d04c-4ad8-aa5d-6d3d4fc680a2","year":2016},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.142712Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:71cc3e0c8a1c42b30920c3c145a014ef6bb9708103a2016927c3df62e42f5630","observation_id":"56163be8-19b4-442a-80b8-bc343e537222","resolution":{"observed_at":"2026-08-14T10:43:46.192873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T10:43:46.173886Z","title":"Beyond pascal: A benchmark for 3d object detection in the wild","venue":null,"work_id":"f848d6d7-028c-4d27-8e77-c9e798f3f29e","year":2014},"citing_paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T10:43:46.146585Z"},"links":{"citing_paper":"/paper/1908.10543"},"observation_digest":"sha256:fbfdc24a646a64167e94357f7e9d5a9c24daccb201ee03ff934fafc37d076153","observation_id":"401b6957-0a48-4507-b7dc-87c53210a314","resolution":{"observed_at":"2026-08-14T10:43:46.179535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.10543","last_updated":"2019-08-28T04:16:52Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T12:32:07.040526Z","submitted_at":"2019-08-28T04:16:52Z","title":"SPair-71k: A Large-scale Benchmark for Semantic Correspondence"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":13},"total_outbound_references":14},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 23 inbound Pith citation observations for arXiv:1908.10543."}