{"as_of":"2026-08-16T18:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c1c1f48b62f83759619831ad50c445130bd1e50620d4e8cdd57706c8141394f3","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T06:03:42.648937Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2605.20645/citation-record","integrity":"/paper/2605.20645/integrity","json":"/paper/2605.20645/citation-record.json","paper":"/paper/2605.20645"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning general- ized segmentation for foggy-scenes by bi-directional wavelet guidance","venue":null,"work_id":"0742dfc8-99e2-4f5e-bd1a-11efa6e69e77","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:85c4f61b1ce5c9edbadec1289b3d9dcbbe3d7f50d4aeffbe1ea1ee615a1545a7","observation_id":"67951039-fd08-4010-860e-416b8dac01ef","resolution":{"observed_at":"2026-05-21T06:03:59.552275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Tsnet: deep network for human action recognition in hazy videos","venue":null,"work_id":"34a2ed9b-33d3-4276-ace4-a5f8aa11b4c0","year":2018},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:7129f6b14989fb294d2f502fc97d4d56d9912b08308422c1593ea86f1debb527","observation_id":"c2d0c26c-4491-4961-88ed-df4770279aed","resolution":{"observed_at":"2026-05-21T06:03:59.562067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Depth-based end-to-end deep network for human action recognition.IET Computer Vision, 13(1):15–22","venue":null,"work_id":"292c7fa3-739c-42f6-a9e5-e58fcd9aa235","year":2019},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:7eef1c616ef25fd59ae504abe79732ad3eaa19d0c27e4cf7b3f13d824bbb9499","observation_id":"22d1d61b-f1d2-4f27-8e3f-74cc6976edac","resolution":{"observed_at":"2026-05-21T06:03:59.525568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Ost: Refining text knowledge with optimal spatio-temporal descriptor for general video recog- nition","venue":null,"work_id":"c533fc16-290a-48df-879f-9e1038e59047","year":null},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:0ea13fc110051b17d2e45d4298109e30eb5e39acc3f0093579e21f14f8991fca","observation_id":"fa61aded-c4f9-4495-9edf-c4bab7545526","resolution":{"observed_at":"2026-05-21T06:03:59.531505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Prompt-based test-time real image dehazing: a novel pipeline","venue":null,"work_id":"2f6202f0-a89f-4ff7-8dd0-ebc832f6c30c","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:a51554047d566badc830d83210df618c91c4ef74f1ebfad40c21f02ef0929362","observation_id":"4d116884-9a93-4b48-8a8e-35fd59cbcb84","resolution":{"observed_at":"2026-05-21T06:03:59.503136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention.IEEE Transactions on Image Pro- cessing","venue":null,"work_id":"0a259194-1481-498c-b6a1-ffbd475905b9","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:88f9c672f20ee65c5476efa6083e520cf95d167c6ef75cc6140e0d22d7034fff","observation_id":"39e33153-063c-4619-8aab-80f53f7f5624","resolution":{"observed_at":"2026-05-21T06:03:59.540489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"A real haze video database for haze level evaluation","venue":null,"work_id":"c0c7b7ec-db65-4827-83d2-b12b7af31a88","year":2021},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:67054d6254a068ca0a20d7fc61487ee7c123e7a90b8ff84ca6553d34a36bf99d","observation_id":"32ebc236-7d31-4e2d-92c1-049064022858","resolution":{"observed_at":"2026-05-21T06:03:59.518787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7e469a36-9008-4e51-ae74-f3b183c182a5","year":2020},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:2d00501a4613e976f75ca9461c15f5f5e8cea5c748a6565ae85b7f5ee7d2c48e","observation_id":"d45134d8-87fe-4bcc-9631-01720292b2be","resolution":{"observed_at":"2026-05-21T06:03:59.512094Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Rgb- event fusion for robust lane detection","venue":null,"work_id":"1915620c-94a4-46af-98d3-2e7c85b0bf81","year":2025},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:78dc026b9b46a405f1f5fcf195295f0588edd74daf3a6f1e5af7f435ae45ccc7","observation_id":"a56cdd16-a8d2-4a04-a503-29c3b2b42791","resolution":{"observed_at":"2026-05-21T06:03:59.565554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Multi-task learning for video surveillance with limited data","venue":null,"work_id":"9621740f-316b-42af-9df7-bb0410313ded","year":2022},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:98e3f478d9285922ed7ef31e44a65d6c4f24d78f3e19e802a23c19cb1eb0df61","observation_id":"5af1866d-1a47-4ead-bd09-20bcac2edab5","resolution":{"observed_at":"2026-05-21T06:03:59.550591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01020","last_updated":"2023-10-02T09:12:39Z","snapshot_observed_at":"2026-08-16T14:55:57.127465Z","submitted_at":"2023-10-02T09:12:39Z","title":"A New Real-World Video Dataset for the Comparison of Defogging Algorithms","version":1},"cited_work":{"arxiv_id":"2310.01020","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.01020","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A new real-world video dataset for the comparison of defogging algorithms.arXiv preprint arXiv:2310.01020","venue":null,"work_id":"9918676c-1091-4e22-bd39-3f809ed486cd","year":null},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"cited_paper":"/paper/2310.01020","citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:65decd316588e7bc938b9c064c48bb31ec48754d7fb438be097f7dabf2f011b2","observation_id":"7dd58460-23b9-4ddc-af1d-12ec9768893b","resolution":{"observed_at":"2026-05-21T06:03:59.200855Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Surveillance face presentation attack detection challenge","venue":null,"work_id":"a532a3ff-da2f-4ca9-8a13-94ae9c535fc2","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:740788cece3a659b50440c67c45f855dfdb51c6646f74837fea62432a25e5e42","observation_id":"d3b38e81-4af3-494a-9cbf-4f82ad7aeb10","resolution":{"observed_at":"2026-05-21T06:03:59.544184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Robust object detection in challeng- ing weather conditions","venue":null,"work_id":"c1ee7f5d-9413-429b-9411-508afae4ec22","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:e7820cb704e5eff3ca713c3223d7b88490915aa10e5dcc09b61c461a2d8906e0","observation_id":"f6cdac01-e8ec-4706-8144-606e8f8b7650","resolution":{"observed_at":"2026-05-21T06:03:59.551527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Populating 3d scenes by learning human-scene interaction","venue":null,"work_id":"5c7f525b-cf7c-4a76-8b2a-9532a0ca35e8","year":2021},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:bf13b49095d4b40f3de522d246814c0827dde7bbdae3890fdb48caab5f96ca20","observation_id":"44cbc3fb-2882-4b12-ab4c-9e2f13274179","resolution":{"observed_at":"2026-05-21T06:03:59.536985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Planning-oriented autonomous driving","venue":null,"work_id":"c3a2f1c8-078b-430c-b4dd-c953f796d27a","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:8cfef0f16154af2e89ec7952241746bea75da94a4e5d8028a95437a5f13f999a","observation_id":"4580ff47-e187-4fba-acd7-fde27b6c03e8","resolution":{"observed_at":"2026-05-21T06:03:59.563034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Hazespace2m: A dataset for haze aware single image dehazing","venue":null,"work_id":"58a96cda-0a0e-4fa9-8da6-2d1aedf33134","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:d56e448d40e0e7dc62573bf6364fd328422e9b8df35a005408a3c72e8b79fa2a","observation_id":"10814a03-e4da-4be6-a900-47cbae825149","resolution":{"observed_at":"2026-05-21T06:03:59.558626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.06950","last_updated":"2017-05-19T12:07:01Z","snapshot_observed_at":"2026-08-15T11:35:18.832923Z","submitted_at":"2017-05-19T12:07:01Z","title":"The Kinetics Human Action Video Dataset","version":1},"cited_work":{"arxiv_id":"1705.06950","doi":null,"metadata_source":"pith","pith_arxiv_id":"1705.06950","snapshot_observed_at":"2026-07-09T11:16:11.423695Z","title":"The Kinetics Human Action Video Dataset","venue":"cs.CV","work_id":"c8a3de61-cfd3-4aeb-bcf7-a0372c015748","year":2017},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"cited_paper":"/paper/1705.06950","citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:6adebe941b55d6723cb460eccf78f7a5497f7b0de5c11c2570baf62bad19f9da","observation_id":"e3b36bba-e3be-4303-8e93-5fa6c5893b08","resolution":{"observed_at":"2026-05-21T06:03:59.185742Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Leveraging temporal contextualization for video action recognition","venue":null,"work_id":"9d804a8b-f5d7-4b4f-a310-473a54422f7b","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:ae74cb268734903935883ff4fdaf87edaea4242cf2a550039db65ca743ad06cc","observation_id":"57b634c3-a561-49e3-a69b-a9208231a9bf","resolution":{"observed_at":"2026-05-21T06:03:59.505845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Hmdb: a large video database for human motion recognition","venue":null,"work_id":"bcf94577-e22b-41b7-a258-68e8518e6c76","year":2011},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:30c912df3ce5214dac71e362499b69edccd42320b2c7ba0b0814e4d9e377584e","observation_id":"5e6b73a1-ad30-4eed-a717-ef8bf283f8a0","resolution":{"observed_at":"2026-05-21T06:03:59.533101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Benchmarking single- image dehazing and beyond.IEEE Transactions on Image Processing, 28(1):492–505","venue":null,"work_id":"d7e4aa4a-ca58-46ed-944a-58b9f26c3d5c","year":2018},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:542f79bad7959da23666644c4aa825124a4d4f747cca2a34ed0362d6a372b847","observation_id":"02df47e9-9f51-4d62-8db3-fe237dfb0a70","resolution":{"observed_at":"2026-05-21T06:03:59.556028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Llama-vid: An image is worth 2 tokens in large language models","venue":null,"work_id":"7fa02e57-8629-4cc8-8dc1-1f30d4a2f113","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:03bb510c946f0b2b8d3dbba8835b8d6c88616f18fa672423886ba567e3662bc7","observation_id":"1aeccccb-0041-4903-bb2e-6105a51777b8","resolution":{"observed_at":"2026-05-21T06:03:59.513144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"A lightweight multi-level rela- tion network for few-shot action recognition","venue":null,"work_id":"b0faed59-a7e3-4d61-a319-b410ef22d709","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:6e4cd2bca588e89667f251b6f165ae36cf1d3ef00e22f940ca3fea569c1a2d3a","observation_id":"aadf6f29-ade7-4925-8b29-0b1d42644500","resolution":{"observed_at":"2026-05-21T06:03:59.554975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14238","last_updated":"2024-10-18T07:40:41Z","snapshot_observed_at":"2026-08-16T13:08:07.829425Z","submitted_at":"2024-10-18T07:40:41Z","title":"Storyboard guided Alignment for Fine-grained Video Action Recognition","version":1},"cited_work":{"arxiv_id":"2410.14238","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.14238","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Storyboard guided alignment for fine-grained video action recognition","venue":null,"work_id":"fa9be715-d530-482a-a493-65bb85ce8eda","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"cited_paper":"/paper/2410.14238","citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:dbd1e9be59d9b3f467ebb1c133d57b1453fd8289c68224f84962b597176e3bad","observation_id":"4134401a-d6fc-4440-ba43-bf8695b4e3aa","resolution":{"observed_at":"2026-05-21T06:03:59.197764Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Narasimhan and Shree K","venue":null,"work_id":"56235bfd-1f69-45d3-8a85-bac7ceac7b36","year":2003},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:43afe1217cd290adf38025dcb4fc75556c2ef933b4b48acb09d4bff798b6b9ce","observation_id":"64caa8a7-4c26-4b66-bd0b-ba41fc654744","resolution":{"observed_at":"2026-05-21T06:03:59.557770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Expanding language-image pretrained models for gen- eral video recognition","venue":null,"work_id":"13d89aaf-86cb-47f3-9357-a7daede8bc2d","year":2022},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:45696fdba29142417b464cbd7cd4acde4f7ffc80d684a82b7018f080f8f4187b","observation_id":"c2682f2d-15cf-4cd0-98ec-7c327004e928","resolution":{"observed_at":"2026-05-21T06:03:59.563713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-08-14T18:53:38.574749Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":"1807.03748","doi":"10.1609/aaai.v36i10.21390","metadata_source":"pith","pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Representation Learning with Contrastive Predictive Coding","venue":"cs.LG","work_id":"7b08a1d4-d565-424e-9c86-6ef244b7b90a","year":2018},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:dc43b8f07fadecd0375427b31b8b51a49179057823991bfb167ba79d70c253a1","observation_id":"fc81f3c0-7534-445c-a21f-7f59eb6cd293","resolution":{"observed_at":"2026-05-21T06:03:59.188892Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-11T10:12:11.384939Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":"2304.07193","doi":"10.48550/arxiv.2304.07193","metadata_source":"pith","pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv2: Learning Robust Visual Features without Supervision","venue":"cs.CV","work_id":"26b304e5-b54a-4f26-be7e-83299eca52e4","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:f2f2be68db91ed53d3dfaa6ef7e14a962ab0f1923a58f140c19c2f19b26f77a2","observation_id":"deba3799-9ca9-4d24-8a60-bd74f0263213","resolution":{"observed_at":"2026-05-21T06:03:59.203648Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Bringing a blurry frame alive at high frame-rate with an event camera","venue":null,"work_id":"3c7cad0f-76a6-4f4c-b996-48a3d6ba8268","year":2019},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:5789b3279d66132a26f712334f1109e7182392f8f4b36d02b3e5f3f4290c2fc6","observation_id":"4859bfee-cec9-4904-9d2a-aef84cab880e","resolution":{"observed_at":"2026-05-21T06:03:59.564736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"b9d0dfec-ff9d-4068-8f55-de2dd371ac38","year":2021},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:ea44495b1d65fdc4fbe23e6ae9a98d068da82cf7c5b078b6ea642b76e8e33aca","observation_id":"33e65dbe-23b1-4fbc-b80c-aed52335e0a0","resolution":{"observed_at":"2026-05-21T06:03:59.536710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Fine-tuned clip models are efficient video learners","venue":null,"work_id":"239075bd-40a6-4f36-879c-22ab0bc366a4","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:26c8f1529173121bddec5e7b21b8a1de679622cace1854c06cde64e62e279630","observation_id":"4d766c97-7081-45f5-94ce-dc9db2ffeb77","resolution":{"observed_at":"2026-05-21T06:03:59.553142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Model adaptation with synthetic and real data for semantic dense foggy scene understanding","venue":null,"work_id":"9cc65ae7-9715-4146-9f7d-bc28e28c8f33","year":2018},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:235be9361f9b2ac58b826ceaebf65b8b71ef031af532f8015eaf71eb27b5c011","observation_id":"f98fb2a6-f22f-4fe9-9646-6bd085a8d66c","resolution":{"observed_at":"2026-05-21T06:03:59.559557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Seman- tic foggy scene understanding with synthetic data.Interna- tional Journal of Computer Vision, 126:973–992","venue":null,"work_id":"4ddc8054-cdd9-4db8-957e-6af9c6299438","year":2018},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:79d48f6db5a6ceb741068e88089ec6d4c6aa606e9adf15b4782d968852a4bb66","observation_id":"fd2d403b-17d9-46c1-89c9-ee7c4a42a83e","resolution":{"observed_at":"2026-05-21T06:03:59.516128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Acdc: The adverse condi- tions dataset with correspondences for robust semantic driv- ing scene perception.arXiv e-prints, pages arXiv–2104","venue":null,"work_id":"ca81495e-4cac-48f6-ad3c-696a813128d8","year":null},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:40db60b27e54bff85b0a8c4d6dfda5396da1e1250f0f57d5baaca5f2e154c27b","observation_id":"9a0fa117-64ec-4b36-b33c-b1998e78d25e","resolution":{"observed_at":"2026-05-21T06:03:59.540849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"A dataset of 101 human action classes from videos in the wild.Center for Research in Computer Vision, 2(11):1–7","venue":null,"work_id":"862de476-6d77-4f94-a206-574dd0c63bb1","year":null},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:732e86e4494c652ac4934d99a7b5b2192bdaeba52e4faea90c5378afbd41ab46","observation_id":"ae923aca-836f-4c79-ad0c-eff0e32009af","resolution":{"observed_at":"2026-05-21T06:03:59.547028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Action recognition in haze using an efficient fusion of spatial and temporal features","venue":null,"work_id":"bdeab560-db92-463f-a831-42760dbbe38c","year":2020},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:39e457ca382e58603e6ffb97dafdda2bb55cf09fc21afe081e61164aeadb225f","observation_id":"4c9b9e08-537c-4180-b85e-a755a07e9a66","resolution":{"observed_at":"2026-05-21T06:03:59.535176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.Advances in neural information processing systems, 35:10078–10093","venue":null,"work_id":"bb65de2a-9640-45be-8f93-d0fd4d6c1e07","year":2022},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:25ebc7d81c587f2ce0cef716de46a7a2abee28d73087063261bb814fe5125755","observation_id":"d0c286ab-9049-463d-8cdc-099288354131","resolution":{"observed_at":"2026-05-21T06:03:59.542819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Light-dehazenet: a novel lightweight cnn architecture for single image dehazing.IEEE transactions on image processing, 30:8968–8982","venue":null,"work_id":"4e62156c-5dc4-4264-aefd-e583bdf72d02","year":2021},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:777b4c2604b342f94e5d2dd3e657a9e825a4d5294b39132c4ce01dd07c07f7b4","observation_id":"34699874-826a-459e-8dc4-dc9f24cf3295","resolution":{"observed_at":"2026-05-21T06:03:59.515133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Actionclip: Adapting language-image pretrained models for video action recognition.IEEE Trans- actions on Neural Networks and Learning Systems","venue":null,"work_id":"ba092865-eadd-4df7-aaa1-61519d193a61","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:20e184e3194251408fca66691db1442eecbaefd9cd4ccf462bf5119db8854cf5","observation_id":"ed680049-fdf4-4f79-8df8-e8b8d26c433b","resolution":{"observed_at":"2026-05-21T06:03:59.522660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"A multimodal, multi-task adapting frame- work for video action recognition","venue":null,"work_id":"8343773d-5997-4722-81e7-4592400b93a2","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:8f98e133a2fab5f694beb340dadfc23dacdb78e4c10585844c4279cef2539cfc","observation_id":"1b76d017-f14a-406a-baf7-c61918b4b416","resolution":{"observed_at":"2026-05-21T06:03:59.556833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Ucl-dehaze: Towards real-world image dehazing via unsupervised contrastive learning.IEEE Transactions on Im- age Processing","venue":null,"work_id":"c189c381-9bf1-4d2a-b843-43c2f33004ed","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:5b62468579487efcd6e8daa217e30db0cafc62b9eec3afbf6d0b567d6f75286b","observation_id":"6523f70d-f003-45d5-912e-f1ef570f263e","resolution":{"observed_at":"2026-05-21T06:03:59.542384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Vita-clip: Video and text adaptive clip via multimodal prompting","venue":null,"work_id":"760fecbe-377f-4c30-bc4a-7dd050e510e3","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:e63f7415e0cf70df2fabd14dec7f240aab1f9efc8161b2c4f4d6477dcefbea34","observation_id":"55c74bb3-f861-455c-8833-8d0f92381618","resolution":{"observed_at":"2026-05-21T06:03:59.560381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.17090","last_updated":"2023-12-28T16:10:25Z","snapshot_observed_at":"2026-08-02T07:14:02.308302Z","submitted_at":"2023-12-28T16:10:25Z","title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","version":1},"cited_work":{"arxiv_id":"2312.17090","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.17090","snapshot_observed_at":"2026-07-08T01:44:26.160588Z","title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","venue":"cs.CV","work_id":"2a0ee74c-0c50-4cd3-91ce-b75cc297715e","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"cited_paper":"/paper/2312.17090","citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:d16ea66ca7be8640b14fb8099f02da2896a31a27d04594a9412b0e1424224849","observation_id":"951a007a-39bf-4f96-83c2-71c1904a7943","resolution":{"observed_at":"2026-05-21T06:03:59.191922Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"What can simple arithmetic op- erations do for temporal modeling? InProceedings of the IEEE/CVF international conference on computer vision, pages 13712–13722","venue":null,"work_id":"ea215769-c8fa-4ee6-a7a5-56bb6ab6b579","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:6738c9a02fe66c9b4f007a6e50ae814103ae7978bbb359c79c1515fa61b4e300","observation_id":"ed65a019-381f-4bdd-8064-eece049f8e44","resolution":{"observed_at":"2026-05-21T06:03:59.554053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Bidirectional cross- modal knowledge exploration for video recognition with pre-trained vision-language models","venue":null,"work_id":"66d61336-26b6-4087-a64b-46088437a827","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:64bdb6fa0284d98a3e7f01af1935d034f544044ad0a12eda44ad92fec0a16e13","observation_id":"4d2fa56a-60eb-4fc6-91b6-13713ba88ad7","resolution":{"observed_at":"2026-05-21T06:03:59.546041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Video dehazing via a multi-range temporal alignment network with physical prior","venue":null,"work_id":"cfb3fd57-34c9-42e5-b224-a1f60f830828","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:c0bf5623d85d3c1547f303e41edffc98a902fa2ed72c0b9b6ef7a2374ebbc13b","observation_id":"435917c8-3d9b-4000-b864-524a9adcbac3","resolution":{"observed_at":"2026-05-21T06:03:59.548832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Language- driven all-in-one adverse weather removal","venue":null,"work_id":"63968a0f-a797-45c0-8a6e-5cce4bfd7b8f","year":2024},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:fce6c00ad5ed2be988d0dfbd4fe0dfae0ca273009ae56f7c707a360125a3e83e","observation_id":"5c66ff12-8c8d-42b0-a39d-3a0b99be1c67","resolution":{"observed_at":"2026-05-21T06:03:59.561389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.03024","last_updated":"2023-02-06T18:59:17Z","snapshot_observed_at":"2026-08-16T15:57:18.631878Z","submitted_at":"2023-02-06T18:59:17Z","title":"AIM: Adapting Image Models for Efficient Video Action Recognition","version":1},"cited_work":{"arxiv_id":"2302.03024","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.03024","snapshot_observed_at":"2026-07-02T16:57:09.595862Z","title":"Aim: Adapting image models for efficient video action recognition.arXiv preprint arXiv:2302.03024","venue":null,"work_id":"2dbe580c-c124-42d5-b966-7758cb340c0d","year":2023},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"cited_paper":"/paper/2302.03024","citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:5ea9be1204e0685a78c15754ca06a6bc320259a60c798a61dac0233458fb685a","observation_id":"119103ea-5fcf-46b9-94a5-4b662a588ead","resolution":{"observed_at":"2026-05-21T06:03:59.194902Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Dehaze- mamba: large multi-modal model guided single image de- hazing via mamba.Visual Intelligence, 3(1):11","venue":null,"work_id":"65799a11-be30-4159-9d20-5b5adcc4bc96","year":2025},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:21720d4b4a0173cf48b199e1e7cae7b7ed15bb81d057b3b3de95e2a661ce67b3","observation_id":"605782a9-ff4e-41be-aa49-85055bae893b","resolution":{"observed_at":"2026-05-21T06:03:59.547997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Learning to restore hazy video: A new real-world dataset and a new method","venue":null,"work_id":"c36abe2f-d082-40ff-921b-5ca4c781e45f","year":2021},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:67556a796032e7ea7ad219dcab6cb8b11228eaa957b955d44857bcbf9e9b0c20","observation_id":"6c34deaf-8b1d-463a-9e1f-2c5caa03ad8d","resolution":{"observed_at":"2026-05-21T06:03:59.517887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-06-05T21:23:00.469572Z","title":"Dehazing evaluation: Real-world benchmark datasets, criteria, and baselines.IEEE Transactions on Image Processing, 29:6947–6962","venue":null,"work_id":"719635a2-7736-4dd0-87c3-5690cfa4d28e","year":2020},"citing_paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-21T06:03:42.648937Z"},"links":{"citing_paper":"/paper/2605.20645"},"observation_digest":"sha256:a973086008f7d65e95f99da9399575e01c257207c7d625607aeac71dd1f34b02","observation_id":"9bb4f279-8199-49a5-814f-630f91d6ed7e","resolution":{"observed_at":"2026-05-21T06:03:59.521603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.20645","last_updated":"2026-05-20T03:09:18Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T10:35:47.638068Z","submitted_at":"2026-05-20T03:09:18Z","title":"Seeing Through Fog: Towards Fog-Invariant Action Recognition"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":7,"verified_fuzzy":42},"total_outbound_references":50},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2605.20645."}