{"as_of":"2026-08-20T08:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e499c47218e4bd39472995a303c49b7851962eca51d2a34e568d5404a8c9025c","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:37:33.989944Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:19:03.529663Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T20:19:04.762757Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"cited_work":{"arxiv_id":"2501.01245","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.01245","snapshot_observed_at":"2026-08-15T20:19:04.762757Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","venue":"cs.CV","work_id":"7affd5ac-c8f3-4fc6-9cd0-5e7e4d0147ec","year":2025},"citing_paper":{"arxiv_id":"2505.13437","last_updated":"2025-05-19T17:58:11Z","snapshot_observed_at":"2026-08-18T08:21:59.232586Z","submitted_at":"2025-05-19T17:58:11Z","title":"FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T20:19:03.529663Z"},"links":{"cited_paper":"/paper/2501.01245","citing_paper":"/paper/2505.13437"},"observation_digest":"sha256:72346a672186c22671d9ad3fdf8a6b870fb1fb3912b9c128611c06d378823fa0","observation_id":"30c29b88-d4f5-4fca-9cf8-92cdf6eb58ca","resolution":{"observed_at":"2026-08-15T20:19:04.855458Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.01245/citation-record","integrity":"/paper/2501.01245/integrity","json":"/paper/2501.01245/citation-record.json","paper":"/paper/2501.01245"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:37:34.476010Z","title":"See https://vicuna","venue":null,"work_id":"d8d3cb81-8c88-4d53-b714-3443a6000ff1","year":2023},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.907841Z"},"links":{"citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:47bd2c794de4102c170dfa02dce4d85f3d9f5cc1d6780473e7393ff67053acab","observation_id":"084a6e37-c872-4a68-ba26-7a9b5df564e1","resolution":{"observed_at":"2026-08-10T22:37:34.481887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.04552","last_updated":"2017-11-29T14:51:40Z","snapshot_observed_at":"2026-08-13T11:54:46.030796Z","submitted_at":"2017-08-15T15:21:53Z","title":"Improved Regularization of Convolutional Neural Networks with Cutout","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.04552","snapshot_observed_at":"2026-08-10T22:37:33.912254Z","title":"In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2341–2352","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.912254Z"},"links":{"cited_paper":"/paper/1708.04552","citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:e264930b5f792829ee8738ec13c2a0c345faedce7614f79ecbfda04f3a3569bd","observation_id":"4f79a517-e34d-4881-b30e-b879115dbd53","resolution":{"observed_at":"2026-08-10T22:37:33.912254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.03378","last_updated":"2023-03-06T18:58:06Z","snapshot_observed_at":"2026-08-14T18:47:26.721223Z","submitted_at":"2023-03-06T18:58:06Z","title":"PaLM-E: An Embodied Multimodal Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.03378","snapshot_observed_at":"2026-08-10T22:37:33.917295Z","title":"arXiv preprint arXiv:2303.03378","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.917295Z"},"links":{"cited_paper":"/paper/2303.03378","citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:d78a07b5598a7b48777a80fa1855e123e973723ab2f4353819dee13d17d106ff","observation_id":"f47afa19-0b36-462a-b084-c44ccfb345fd","resolution":{"observed_at":"2026-08-10T22:37:33.917295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.01897","last_updated":"2022-08-03T08:01:55Z","snapshot_observed_at":"2026-08-20T01:42:50.791959Z","submitted_at":"2022-08-03T08:01:55Z","title":"Combined CNN Transformer Encoder for Enhanced Fine-grained Human Action Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.01897","snapshot_observed_at":"2026-08-10T22:37:33.931226Z","title":"arXiv preprint arXiv:2208.01897","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.931226Z"},"links":{"cited_paper":"/paper/2208.01897","citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:4dae85f61e0e840dd13b2972303320ff86e8903b8eb92cdb1b9a91ce87f06925","observation_id":"915ee6af-cd7b-4b20-b759-8362c0016d8b","resolution":{"observed_at":"2026-08-10T22:37:33.931226Z","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":"2026-08-10T22:37:34.407521Z","title":"In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, 9568– 9578","venue":null,"work_id":"a333bd73-5cbb-4388-8ff3-ccf50a5f88bf","year":null},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.945815Z"},"links":{"citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:bab45ace7537feff5ec83ac2a95b8739c013a198758b5eb65cc335ce8dbfd613","observation_id":"5bfdad23-8556-4a7d-b21d-d49b20336729","resolution":{"observed_at":"2026-08-10T22:37:34.417401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T22:37:34.387893Z","title":"IEEE Access","venue":null,"work_id":"8ddad137-1ee0-4df3-82ff-d2c93090412e","year":2021},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.950371Z"},"links":{"citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:91d03f7c11371b62d821ec3bf1074cc768033749cc306c616e27689c2e10382a","observation_id":"53bc015a-3cfa-4e93-aec8-f0d577dbe6be","resolution":{"observed_at":"2026-08-10T22:37:34.395969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T22:37:34.368190Z","title":"InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 26077–26087","venue":null,"work_id":"4dfe3a23-3358-4ee4-8ccf-c2c3965dbbdf","year":2022},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.954803Z"},"links":{"citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:f072e95c20bd40e447399a9974d111192b971f93748e3a0102ec670d8f44dda7","observation_id":"83020ceb-ef7e-4bd7-9694-efd5b80cf17f","resolution":{"observed_at":"2026-08-10T22:37:34.374188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04509","last_updated":"2026-04-20T02:03:24Z","snapshot_observed_at":"2026-08-02T21:14:32.237997Z","submitted_at":"2024-10-06T14:59:09Z","title":"ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04509","snapshot_observed_at":"2026-08-10T22:37:33.961647Z","title":"In Proceedings of the IEEE/CVF interna- tional conference on computer vision, 7209–7219","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.961647Z"},"links":{"cited_paper":"/paper/2410.04509","citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:b03fda075c86abf6c0bb57f3d1993e2df266c22008299d83719faf817184320f","observation_id":"9c605137-5d0b-461f-a0c0-26dfbf061253","resolution":{"observed_at":"2026-08-10T22:37:33.961647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03166","last_updated":"2024-10-24T22:35:27Z","snapshot_observed_at":"2026-08-16T15:18:09.804784Z","submitted_at":"2023-07-06T17:47:52Z","title":"VideoGLUE: Video General Understanding Evaluation of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03166","snapshot_observed_at":"2026-08-10T22:37:33.978829Z","title":"arXiv preprint arXiv:2307.03166","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.978829Z"},"links":{"cited_paper":"/paper/2307.03166","citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:6353e77a63c12400501f1a4d3da4c91e439b46c0b376f861f66dab80ae361b90","observation_id":"bd4c1bb8-80d1-4d77-9dee-c730f417f8b3","resolution":{"observed_at":"2026-08-10T22:37:33.978829Z","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":"2026-08-10T22:37:33.984143Z","title":"run- ning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.984143Z"},"links":{"citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:f069b90a5aad17414415a1ff038763d11baca8998cc45092b9f739773b688ddd","observation_id":"fd6903af-9981-4dfb-a805-811385cccc95","resolution":{"observed_at":"2026-08-10T22:37:33.984143Z","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":"2026-08-10T22:37:34.348329Z","title":"forward” vs. “backward","venue":null,"work_id":"78b02679-a764-41a5-80a3-a36d06b0518a","year":null},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.989944Z"},"links":{"citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:81b5d5ddcc2797d9760aaaafca0da01256d39eb6825f07352e756c26ca53b291","observation_id":"c74e98ee-91df-437a-b8fd-15291f28c71e","resolution":{"observed_at":"2026-08-10T22:37:34.353289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-10T22:37:34.431350Z","title":"Hong, J.; Fisher, M.; Gharbi, M.; and Fatahalian, K","venue":null,"work_id":"0393638b-67dc-40f0-bb69-a8126a78e53b","year":2021},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":329,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.926647Z"},"links":{"citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:d1230725f4212acf4ffdccfbb65a8aef7fb3b2f3dfe1b840892165e2fca1d077","observation_id":"5be50374-cfd3-46e9-85a9-6eed03e1ea57","resolution":{"observed_at":"2026-08-10T22:37:34.437249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.06329","last_updated":"2021-04-19T17:59:03Z","snapshot_observed_at":"2026-08-20T01:41:11.313693Z","submitted_at":"2021-01-15T23:29:57Z","title":"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.06329","snapshot_observed_at":"2026-08-10T22:37:33.936316Z","title":"arXiv preprint arXiv:2101.06329","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.936316Z"},"links":{"cited_paper":"/paper/2101.06329","citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:1afa6be1c7d1fe68fdc0c43785c4409f1f4fde88f190c2b79cf299f27a03ae0b","observation_id":"c8117453-c2de-4cbc-94f4-dae5a944dee0","resolution":{"observed_at":"2026-08-10T22:37:33.936316Z","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":"2026-08-10T22:37:34.450374Z","title":"In European conference on computer vision, 242–259","venue":null,"work_id":"79d6a0b4-bae0-4219-8db7-6ed35c865e04","year":2017},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.922164Z"},"links":{"citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:883f06bd9826ce87e8b222e69961524b7c5ac911a7e325310fc363f6ed3a675e","observation_id":"689cd4c8-2f6e-4a32-a9c6-570889a5d0b4","resolution":{"observed_at":"2026-08-10T22:37:34.457115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.09478","last_updated":"2023-11-07T18:25:48Z","snapshot_observed_at":"2026-08-17T13:04:04.064087Z","submitted_at":"2023-10-14T03:22:07Z","title":"MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.09478","snapshot_observed_at":"2026-08-10T22:37:33.901652Z","title":"arXiv preprint arXiv:2310.09478","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.901652Z"},"links":{"cited_paper":"/paper/2310.09478","citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:13ba06e501f913996899dbe9dbd2acbc715c660db295ea3712f6c9a1ba7793f7","observation_id":"e0c82653-6121-4f62-b930-296d5db7f578","resolution":{"observed_at":"2026-08-10T22:37:33.901652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01121","last_updated":"2024-04-01T13:55:44Z","snapshot_observed_at":"2026-08-19T02:45:13.524258Z","submitted_at":"2024-04-01T13:55:44Z","title":"CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening","version":1},"cited_work":{"arxiv_id":"2404.01121","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.01121","snapshot_observed_at":"2026-08-10T22:37:34.216975Z","title":"CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening","venue":"cs.CV","work_id":"e4ee46d8-0ea2-4e1d-8624-a9492b7e4347","year":2024},"citing_paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-10T22:37:33.940878Z"},"links":{"cited_paper":"/paper/2404.01121","citing_paper":"/paper/2501.01245"},"observation_digest":"sha256:2ddfee0e0d04f4f09cf1d808685b3a9d15cc0a66538572f8cb53669384c0d418","observation_id":"1667a234-4d85-4b5c-bd30-39e2921aa469","resolution":{"observed_at":"2026-08-10T22:37:34.226854Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.01245","last_updated":"2025-01-02T13:12:12Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-20T01:41:50.115714Z","submitted_at":"2025-01-02T13:12:12Z","title":"SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":7},"total_outbound_references":16},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2501.01245."}