{"as_of":"2026-08-19T20:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7d9f6d57c8581ea4f9568161fb0f344dd5c5d046098f676fa373ecf60f82da47","coverage":[{"denominator":72,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":72,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-14T20:18:36.505585Z","state":"measured"},{"denominator":72,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":72,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2605.13202/citation-record","integrity":"/paper/2605.13202/integrity","json":"/paper/2605.13202/citation-record.json","paper":"/paper/2605.13202"},"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":"Tsm: Temporal shift module for efficient video understanding","venue":null,"work_id":"55112a92-9b94-4a32-b752-9215014cd9e5","year":2019},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:7926f7c8a0f01b7099080f795c580c496c96c29750eb11c2e1ad6d99789b3312","observation_id":"0b6d32a2-85c4-4037-b240-bea2801acb5f","resolution":{"observed_at":"2026-05-15T16:20:11.303326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Learn- ing spatiotemporal features with 3d convolutional networks","venue":null,"work_id":"e1752018-92e3-4798-bb46-cb3021d58b3a","year":2015},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:8510b63a30ad2251d5c059658ed17af9e9d9bb17eb6790574a6b527f3ab593a6","observation_id":"782007eb-4b6b-4c04-bb08-988356eaf19f","resolution":{"observed_at":"2026-05-15T16:20:11.278140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07750","last_updated":"2018-02-12T17:10:11Z","snapshot_observed_at":"2026-08-14T20:59:05.330633Z","submitted_at":"2017-05-22T13:57:53Z","title":"Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset","version":3},"cited_work":{"arxiv_id":"1705.07750","doi":null,"metadata_source":"pith","pith_arxiv_id":"1705.07750","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset","venue":"cs.CV","work_id":"b63da55f-0b70-4fe5-9b38-962f6225bc0c","year":2017},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"cited_paper":"/paper/1705.07750","citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:339ae3c189e396185b55475e8bedee2bc28f5e768eb3bd9968ecfaf41e652be4","observation_id":"f7b25136-bc58-4cd7-a4ca-031014bdb9c4","resolution":{"observed_at":"2026-05-14T20:19:27.131260Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Hu- man action recognition from various data modalities: A review","venue":null,"work_id":"e7ba28a4-9d4c-455f-9efa-a88059b2f3a8","year":2023},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:464be40492eacfb248666d28457361ddf0abc7dd75475b415957e12c265b050f","observation_id":"331199e8-a87f-4fb9-b437-99c234faaa36","resolution":{"observed_at":"2026-05-15T16:20:11.281088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Vivit: A video vision transformer","venue":null,"work_id":"8ae80f3c-4007-4ae9-9e96-f27801d9ce20","year":2021},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:88bfbd75f3bbd9cc1310ac50fac8376ca887a840b53727d3431537d8b45f669b","observation_id":"8e5b7041-73e5-4a29-ad6f-b38b4a1fd673","resolution":{"observed_at":"2026-05-15T16:20:11.359774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Compound memory networks for few-shot video classification","venue":null,"work_id":"81f102e7-e059-4635-8f45-765c3e6adf8b","year":2018},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:a0b0c461ad6c31098731ab09c9d3d5825c8e49bb63797a0360b550a7265800ab","observation_id":"ebed8b66-ecdd-4925-b46b-571dc8301cce","resolution":{"observed_at":"2026-05-15T16:20:11.352214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Few-shot video classification via temporal alignment","venue":null,"work_id":"45cd5f90-2597-43df-8589-0d201fda89e8","year":2020},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:bba7bde63c012898cb00b01cda685b7ba523abf3224774f534cf172c1a62c025","observation_id":"761a614e-df8c-448b-8e52-bd17636705a3","resolution":{"observed_at":"2026-05-15T16:20:11.243706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Hybrid relation guided set matching for few-shot action recognition","venue":null,"work_id":"b14f6775-bb21-4285-8f5c-fffabf6c5cc7","year":2022},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:080ffc7b81f9c90cb2a260a57d5e2215cb01316edacf0d9ff76912cdee48bda8","observation_id":"afe96af2-8f6e-4cfd-babb-b537b198f414","resolution":{"observed_at":"2026-05-15T16:20:11.333137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-04T22:00:09.709642Z","title":"Prototypical networks for few-shot learning","venue":null,"work_id":"d4da3383-f4a5-40f7-a3cc-734059d911eb","year":2017},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:4788b5c374b85bd5f5a1129e9ecdf735602e9d6be04f2fc983b20904d31ba79d","observation_id":"58fcb094-2893-428e-bcb1-31ab56aad7ba","resolution":{"observed_at":"2026-05-15T16:20:11.344723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Matching compound prototypes for few-shot action recognition","venue":null,"work_id":"fd88cf4c-e739-4db0-ba64-a2bf88842690","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:b262294158d97227cca7f70fee4cc3b95a561a17eea07756efc3aba7966255f6","observation_id":"6c7cb5c6-719f-427a-a734-11037a9cb683","resolution":{"observed_at":"2026-05-15T16:20:11.184373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Tarn: Temporal attentive relation network for few-shot and zero-shot action recognition","venue":null,"work_id":"8420a3b7-3f3a-4f42-8594-8cc344a2ab48","year":2019},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:3631272d2fb76f94421527fef58cb78d81fcd0a183c914d7e4ace30445330a2d","observation_id":"2e9eea57-f0a0-4099-97aa-1cd6f66f743d","resolution":{"observed_at":"2026-05-15T16:20:11.230948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05956","last_updated":"2025-04-08T12:11:11Z","snapshot_observed_at":"2026-08-16T12:43:02.297785Z","submitted_at":"2025-04-08T12:11:11Z","title":"Temporal Alignment-Free Video Matching for Few-shot Action Recognition","version":1},"cited_work":{"arxiv_id":"2504.05956","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.05956","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Temporal alignment- free video matching for few-shot action recognition","venue":null,"work_id":"fce88257-12e0-47b9-8d0c-acb23e91561e","year":2025},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"cited_paper":"/paper/2504.05956","citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:7c6b5b9bb67157dc98953f0d0ca689fe07d75ce537244c8c350cba9dd64d747a","observation_id":"09921744-438b-4292-afde-c775856cf229","resolution":{"observed_at":"2026-05-14T20:19:27.136951Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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 supervision","venue":null,"work_id":"9ea30df5-91c8-4f81-b614-338b7f269fc3","year":2021},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:17cc1f6022d98c657615aaf8fcd7fff542d889dab3fb83c586a8557af15db3e5","observation_id":"fbc62a75-0cbf-4729-923f-2502cf4773f9","resolution":{"observed_at":"2026-05-15T16:20:11.262032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Clip-guided prototype modulating for few-shot action recognition","venue":null,"work_id":"8ad31efc-f483-4e12-b70f-f171206906e6","year":1912},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:99a1bc67d12529526c02a4ba841b5426d1e40f02fb5054998f2c639ac2c5568d","observation_id":"dcc9f72f-d2ab-4572-a6cc-24fc923de8fc","resolution":{"observed_at":"2026-05-15T16:20:11.168145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Temporal-attentive covariance pooling networks for video recognition","venue":null,"work_id":"2f41cd0e-0caf-4fc9-bb88-0dfd46196079","year":2021},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:6c9b4986ad7e3dcf23ccc5a024829948c61d49e3fa8b522eb764f0463c02e2ea","observation_id":"ba65d287-859d-4063-bf71-e527351288a1","resolution":{"observed_at":"2026-05-15T16:20:11.336877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Compound prototype matching for few- shot action recognition","venue":null,"work_id":"d8fc1af4-8fb1-4eab-a033-85d32479ae6b","year":2022},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:393cc1c0afd249a09b411109f560d28190670f4cec23587a2abcfe12f88cce05","observation_id":"7d8570dd-af83-4ad2-b513-c153c93101f6","resolution":{"observed_at":"2026-05-15T16:20:11.172878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Slowfocus: Enhancing fine-grained temporal understanding in video llm","venue":null,"work_id":"b8b5eef3-e8a3-4d2b-93ce-d750a41baabd","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:cb7bcdcd271f866b4eadcc3eb554a21056f0c543242282741bc3bd377fa53265","observation_id":"89acf359-8bb3-43c8-ab1f-d09c3917b59b","resolution":{"observed_at":"2026-05-15T16:20:11.341033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.02077","last_updated":"2025-03-05T13:43:07Z","snapshot_observed_at":"2026-08-17T04:34:28.091318Z","submitted_at":"2024-05-03T13:10:16Z","title":"MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition","version":4},"cited_work":{"arxiv_id":"2405.02077","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.02077","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mvp-shot: Multi-velocity progressive-alignment framework for few-shot action recognition","venue":null,"work_id":"e98f53fa-f7f9-4bb1-a504-efa6907624a9","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"cited_paper":"/paper/2405.02077","citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:f847602fbe7043d80477507834e3a9eaa3b3d35d05252e83bbdb04133fae90fb","observation_id":"4ce3e985-305e-40a6-ab2f-475c3a6fc97e","resolution":{"observed_at":"2026-05-14T20:19:27.125731Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12475","last_updated":"2024-08-22T15:13:27Z","snapshot_observed_at":"2026-08-16T13:24:33.749954Z","submitted_at":"2024-08-22T15:13:27Z","title":"Frame Order Matters: A Temporal Sequence-Aware Model for Few-Shot Action Recognition","version":1},"cited_work":{"arxiv_id":"2408.12475","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.12475","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Frame order matters: A temporal sequence-aware model for few-shot action recognition","venue":null,"work_id":"3a22a349-308c-4ee1-a438-9a08b1d2854c","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"cited_paper":"/paper/2408.12475","citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:4bace6f05fa67e5f91ec660c0c4e5d98eaafdfcbc2c2e0117e35ff385404e2b9","observation_id":"6524010e-db46-4bac-bc03-b3febc068df2","resolution":{"observed_at":"2026-05-14T20:19:27.119259Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Mamba: Linear-time sequence modeling with selective state spaces","venue":null,"work_id":"b71a16a9-fa16-4c66-831e-da7cd55adb9b","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:6aecbe90382aa0ab115813c9b48afbfad3a3358997da0342feaaa6cfa05bb589","observation_id":"32451262-59a5-408c-bc2a-afde6c18e583","resolution":{"observed_at":"2026-05-15T16:20:11.319294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Reallocating and evolving general knowledge for few-shot learning","venue":null,"work_id":"2daf9239-1a9b-4b4e-a37b-b3de23bbb04a","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:1bc84816e389aef6bca4928e333d010781632993ae9658601dbb0a80e7f52fbf","observation_id":"373e4341-fae3-4e14-aefc-424100435b49","resolution":{"observed_at":"2026-05-15T16:20:11.204161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Few-shot cross-domain object detection with instance-level prototype-based meta-learning","venue":null,"work_id":"5c96692d-01e7-4ab6-b52b-65963e4b851d","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:7ec241fab8d0584cc5ef4cf3cee870cad3106b4a700596f69768012bbe012c24","observation_id":"b6ea9588-e1bf-42fe-8f6b-ca93a37b4ab7","resolution":{"observed_at":"2026-05-15T16:20:11.238296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Matching networks for one shot learning","venue":null,"work_id":"45d40994-1ea0-44f8-8d5c-951e2e4a591d","year":2016},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:b41b8fda33fb2202808583066822c2a7252749b231cb4ce0cfa9d777b7d92bc6","observation_id":"1d7ad3c0-63df-4952-ab69-13968fc32cbb","resolution":{"observed_at":"2026-05-15T16:20:11.163263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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 compare: Relation network for few-shot learning","venue":null,"work_id":"723605db-412c-42c3-988a-ea6a2cb6e80e","year":2018},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:f472a577b57ea426822a55d78e14bc3bdb251881321ee6bef68c09379da7d7b8","observation_id":"887998f9-94b4-4230-9dc0-d167771d430a","resolution":{"observed_at":"2026-05-15T16:20:11.234155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2603.05952","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unify the views: View-consistent prototype learning for few-shot segmentation","venue":null,"work_id":"6fd14185-8888-4dc8-9984-c7c22d5b3cef","year":2026},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:e5284359c3b32926a08f1b4e9c9ead1ad9bfac2feaad66dd20f5cd1557e41527","observation_id":"f1ca4b44-6fb0-46d1-bffe-6005a2e444f8","resolution":{"observed_at":"2026-05-14T20:19:27.152811Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Few-shot metric learning: Online adaptation of embedding for retrieval","venue":null,"work_id":"2ee5b936-3078-4d7d-a62c-c83dcd7ffd9d","year":2022},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:755a7c3dcdfe6d90807b9485c2b3293c84294ff57be3f439580229281a99e2ea","observation_id":"9b23aec7-8d19-47b0-a3a3-aaa550cfefbb","resolution":{"observed_at":"2026-05-15T16:20:11.329122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Revisiting few-shot learning from a causal perspective","venue":null,"work_id":"0d126c74-8aee-4c16-b7d0-d23b10d73379","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:31d44278dadd4937da01cc70f5d6bc2fcb5811d1ce300c2d639a75980d292ca5","observation_id":"89137c42-397f-400c-967a-2a921b0b604a","resolution":{"observed_at":"2026-05-15T16:20:11.298278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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 attention-guided pyrami- dal features for few-shot fine-grained recognition","venue":null,"work_id":"d1f1b23c-e183-4e97-b8cb-00f13961ab3a","year":2022},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:321e095264e169f9a6b5e5e5eb446a16e8690911366a3dc0b2a99b1a23ffdb65","observation_id":"e7692148-cf22-41a6-8a29-d0d15c41123d","resolution":{"observed_at":"2026-05-15T16:20:11.305565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Blockmix: meta regularization and self-calibrated inference for metric-based meta-learning","venue":null,"work_id":"0498e83c-051f-426b-8763-2924620f7def","year":2020},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:d244f0a7260ffda83c9d6c3e9c16b97d4f3aaa4081277756784d5440e00e9ffb","observation_id":"ece0df93-d7e3-4b40-a649-84bb848787ac","resolution":{"observed_at":"2026-05-15T16:20:11.245798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T16:16:20.486923Z","title":"Model-agnostic meta-learning for fast adaptation of deep networks","venue":null,"work_id":"3ca7bd26-6782-40e2-8840-5f3dff346e87","year":2017},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:c9eb2c05059d324d8aceee215217f1dbc62fe12ddedf6ac5bf26e578e0b2a784","observation_id":"57eea820-127f-4b4e-a22e-48c1d5ef19e5","resolution":{"observed_at":"2026-05-15T16:20:11.348555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Meta-exploiting frequency prior for cross-domain few-shot learning","venue":null,"work_id":"1b357023-3187-4e87-81c0-338ab49c9cde","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:c6711612a7a3de5a75f7fbb7a479c89ba38c7d69e23b7e5841836aaad58debc5","observation_id":"bc7efe1d-2c5d-4ef3-bf9f-edb057c01340","resolution":{"observed_at":"2026-05-15T16:20:11.241977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"On the stability and generalization of meta- learning","venue":null,"work_id":"400c05cb-cea2-44cb-82f5-fea3fe500d7b","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:a5decad3e805b6528fc9a42f0070421e38e56791f79519ed81ad948158ef7317","observation_id":"0868a76d-6e2d-4394-944f-9807a590b1c0","resolution":{"observed_at":"2026-05-15T16:20:11.186946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Few-shot action recognition via multi-view representation learning","venue":null,"work_id":"5e36e248-df0b-4dd1-815d-65f33178a9e1","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:36927dd58ae2dbe2788a12fd3401a533c779be9ed1c947be8dd4845e2ffb3c11","observation_id":"230b5a6f-97d2-4e84-b4cb-e53ed9931559","resolution":{"observed_at":"2026-05-15T16:20:11.315549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Task- adapter: Task-specific adaptation of image models for few-shot action recognition","venue":null,"work_id":"69237d86-6d0b-481f-8bf0-42705213c15d","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:657ae85bee376faa19fafd3bd017399c26d23f806ffa82093edb29e603af0c09","observation_id":"0b4e7268-b6fc-46ae-8375-497ec6a74929","resolution":{"observed_at":"2026-05-15T16:20:11.311508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Cross-modal contrastive pre-training for few-shot skeleton action recognition","venue":null,"work_id":"14e27d16-7878-43fd-b90b-69f5a3c84994","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:2f71a14ca56c4850401666f0882e96aa05834bed7467711e611d741d44f39685","observation_id":"a4e5a76f-3848-43d3-96a4-c0aababec3a3","resolution":{"observed_at":"2026-05-15T16:20:11.355774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Dual-recommendation disentanglement network for view fuzz in action recognition","venue":null,"work_id":"91772da2-4748-4dbd-bc75-41b9dc4140a7","year":2023},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:bd44077348f674281be6f4d375aea0f8b0bd7918e9256535c401f1cedb1cc401","observation_id":"23f33515-22cc-40af-8f5b-e3fa280ba7c6","resolution":{"observed_at":"2026-05-15T16:20:11.289118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Motion- consistent representation learning for uav-based action recognition","venue":null,"work_id":"96c98190-a622-4d75-98ca-d09a543cb150","year":2025},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:d77a33ffc7b1f5f28e9665d5577e5f8a8d9078d99f4d3ca3af3cc46b4c9573d8","observation_id":"4b12dbd9-4fca-43b6-9893-b16b035a8657","resolution":{"observed_at":"2026-05-15T16:20:11.265273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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 guided adaptive meta-fusion network for few-shot video recognition","venue":null,"work_id":"d12488bd-e42b-461b-9fac-2b749327fd50","year":2020},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:27346cde85b4b38c5506d38b3fb6c4e3f5a031866d5ebc1bca0030617b1ff0eb","observation_id":"7ff1d615-e4c8-4509-bda7-99b613eb980b","resolution":{"observed_at":"2026-05-15T16:20:11.274686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Temporal-relational crosstransformers for few-shot action recognition","venue":null,"work_id":"976fba8f-8f94-4ae9-9965-0e951b83e213","year":2021},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:b44a31e7ef6c3c9cd610ed92767f7ee17a2fc78e6c633c2e22bc002bc42764e2","observation_id":"fa0083f1-3164-4e5c-9616-3cb2a8664b7e","resolution":{"observed_at":"2026-05-15T16:20:11.225172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Semantic-guided relation propagation network for few-shot action recognition","venue":null,"work_id":"eea73dbe-c6bf-4d75-b088-68d82384b0ad","year":2021},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:970dce7cc18c33963720c15924933edc9d1b3e9cea51c90fd21052e1d4fd803b","observation_id":"5c0f11ef-3a93-476c-841f-f82e8c659ef5","resolution":{"observed_at":"2026-05-15T16:20:11.293430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Molo: Motion-augmented long-short contrastive learning for few-shot action recognition","venue":null,"work_id":"95db747f-35f2-4905-aea0-6784b3a05f03","year":2023},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:796e8aecbb3851cae50c8815bba01611e66fd237105316dbd639e909257e2418","observation_id":"a0175c96-d8b9-43ec-a20c-a9fe96f6cbce","resolution":{"observed_at":"2026-05-15T16:20:11.147180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"M3net: multi- view encoding, matching, and fusion for few-shot fine-grained action recognition","venue":null,"work_id":"e27438f6-365c-465b-a64e-54c92bf2212b","year":2023},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:a08939a6391ae061d1de8f29bf3002691e56e8e3427c3596cae838259095fce7","observation_id":"caf83686-fe8d-43c5-8c8b-24e670375e2e","resolution":{"observed_at":"2026-05-15T16:20:11.247923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Hierarchy- aware interactive prompt learning for few-shot classification","venue":null,"work_id":"2e81f349-cc9e-4964-a72f-aacc41ac5b21","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:6a312ac659b39626c9e70fc8382c4e2f1334266758a231214fe8804d9a75f3db","observation_id":"4d19e786-b05b-473e-8cbb-5549d8339ed7","resolution":{"observed_at":"2026-05-15T16:20:11.226091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Rethinking few-shot adaptation of vision-language models in two stages","venue":null,"work_id":"d63a26df-67a4-41d2-a788-839866f991bd","year":2025},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:a10c38af1fc6e83e09f28033c3f1d4843ac96731e3d52f29fbd50b7af6369afe","observation_id":"54650c7a-381f-4801-bfc7-33e0ed5edd0a","resolution":{"observed_at":"2026-05-15T16:20:11.212305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Connecting giants: synergistic knowledge transfer of large multimodal models for few-shot learning","venue":null,"work_id":"10b85a51-3df7-477a-9618-b2003d6e982c","year":2025},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:7c74571722e2351329a23d693f39f49923f8da713884dd91c59cfbbb6feb70d8","observation_id":"70e3e589-39b3-4635-bcb2-076be85b088c","resolution":{"observed_at":"2026-05-15T16:20:11.087132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Cross-modal proxy evolving for ood detection with vision-language models","venue":null,"work_id":"515dd7bc-989d-4cf5-ab76-e0d7f5fdfaae","year":2026},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:b6c3c2e5ee6236d0b34ec0adcfc4495165caf6d35adff6ddb7d866b87b26467a","observation_id":"c079151f-f955-4019-88ab-3730fc8a5f3f","resolution":{"observed_at":"2026-05-15T16:20:11.277559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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-speed global contextual subspace matching for few-shot action recognition","venue":null,"work_id":"0ff07e8b-6c50-4f6d-ab3c-538624fafc61","year":2023},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:18a9e57a5dff178c13d6987eedce6cabecd2c1921059ace48e790aaf5802c6fa","observation_id":"a4d50fec-8700-4d08-b41b-ae8aa10316e2","resolution":{"observed_at":"2026-05-15T16:20:11.188482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"On the importance of spatial relations for few-shot action recognition","venue":null,"work_id":"649a8fb1-1003-4ce0-bad1-71910d97b72a","year":2023},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:c8ca95e57c8744457335b5df5ce38967e340a3167d6f815581a337b6f1f799c3","observation_id":"6dbafe78-c9fd-49dd-9782-df38f97ac4e8","resolution":{"observed_at":"2026-05-15T16:20:11.179853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Combining recurrent, convolutional, and continuous-time models with linear state space layers","venue":null,"work_id":"3de7e14c-6113-4485-ba07-8f6c4c023722","year":2021},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:a81f6ffa2b308bc0897bbaf9f6a4e8c5300037578e8bd26df4b96a04540f2fd6","observation_id":"48e84e90-c246-4e0b-9833-60c20e7abf2a","resolution":{"observed_at":"2026-05-15T16:20:11.260155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Efficiently modeling long sequences with structured state spaces","venue":null,"work_id":"2f344596-1fd1-4903-bb92-f8b5bf583d4d","year":2022},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:95685576165d3b9e888621ee1e9e9af816f040eb8e951e04a58ed15b781db86d","observation_id":"114569dc-e8e8-41bd-b9b3-11957465b684","resolution":{"observed_at":"2026-05-15T16:20:11.164642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Selective structured state-spaces for long-form video understanding","venue":null,"work_id":"5c9dc3ec-564c-48c1-b7a8-44fe59fe9d93","year":2023},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:95126b976a2979aaf2f5000f8c80a7ea4c2df72b7d20134882682b78419b195d","observation_id":"b902d8bb-0a9c-4569-a53f-6e4652920b7b","resolution":{"observed_at":"2026-05-15T16:20:11.123096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Transformers are ssms: generalized models and effi- cient algorithms through structured state space duality","venue":null,"work_id":"665c0853-ad67-4443-902e-bf6b5409442f","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:0052ff8622cd8e25d74e0592ca4bfc2dad055d58591dc291fc8c54f124b08475","observation_id":"29c7e9a8-8f41-4f5c-8496-94277c5a80b7","resolution":{"observed_at":"2026-05-15T16:20:11.255976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Attention is all you need","venue":null,"work_id":"85fabf64-6327-466b-8b91-938aae48e35f","year":2017},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:dd1d0577f1a8563a66f5548ed531493495eaee4d77c5ff2d39231fa5b14c5e1c","observation_id":"c4547f4c-e083-4667-89ee-39cf4bffa28e","resolution":{"observed_at":"2026-05-15T16:20:11.323223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07992","last_updated":"2024-05-20T16:36:21Z","snapshot_observed_at":"2026-08-16T13:53:03.800284Z","submitted_at":"2024-05-13T17:59:56Z","title":"MambaOut: Do We Really Need Mamba for Vision?","version":3},"cited_work":{"arxiv_id":"2405.07992","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.07992","snapshot_observed_at":"2026-06-28T19:32:35.269755Z","title":"Mambaout: Do we really need mamba for vision?","venue":null,"work_id":"cbf63d57-510f-4f66-aad8-aa9c21b685fd","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"cited_paper":"/paper/2405.07992","citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:e5134a1adb7fca6b3d14e18800b59526f7bd8112f894694d13f62c146969b8b5","observation_id":"daddd1c3-64fe-41ab-9fc0-1f5ee364d0ad","resolution":{"observed_at":"2026-05-14T20:19:27.159180Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Vision mamba: efficient visual representation learning with bidirectional state space model","venue":null,"work_id":"1313e24f-51d2-47aa-9b9e-a06615a6bf89","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:6deb49c8d9546e192a400d588113c2438cec203168ff5347798334363384da12","observation_id":"cd2626b7-c242-4317-a605-696724fe7384","resolution":{"observed_at":"2026-05-15T16:20:11.285041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Vmamba: Visual state space model","venue":null,"work_id":"80b39c44-e058-4a47-ba6d-61a690a609aa","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:17f2109b394b8379111abb361bd2cf968da5ec1204e3b11f4de080177199f71f","observation_id":"c66d5898-699d-4329-a109-71e8cb2e83fd","resolution":{"observed_at":"2026-05-15T16:20:11.273858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Videomamba: State space model for efficient video understanding","venue":null,"work_id":"61227b84-5a54-407e-92c8-c079c893f0ce","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:8c077f2ff249bd580449d3c6030f6dba4d750477cbb0407cb09fa9c48e319f6d","observation_id":"59f19647-8228-41ac-aa98-bcc3e4c96d16","resolution":{"observed_at":"2026-05-15T16:20:11.203509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Mamba-adaptor: State space model adaptor for visual recognition","venue":null,"work_id":"99b0286d-0faf-45c9-ab75-b3f7c0eb1222","year":2025},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:42498e9eb100291b8bf9a1b03dc42ba099facdc3e9156c4f8ca0f79c3681e97c","observation_id":"721f2129-a002-4ce6-9f81-7fc2e4b0d3d9","resolution":{"observed_at":"2026-05-15T16:20:11.169868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Mambavision: A hybrid mamba- transformer vision backbone","venue":null,"work_id":"66c0374b-754f-4812-9d62-f28074f49381","year":2025},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:46dbfd4529347e35d5722a20f72f1dc45c2630379108d99501749af3e28d55b4","observation_id":"9e7d851c-51fa-4cd1-aa9a-60f07ca22f41","resolution":{"observed_at":"2026-05-15T16:20:11.301270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Groupmamba: Efficient group-based visual state space model","venue":null,"work_id":"c3ae759e-e239-416c-a611-ceec01a86cb0","year":2025},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:1fbf9a8f2a435b245235d8ba6c7d03f72359342711eae5542a27b3e3fb5669ba","observation_id":"9bdcd0c0-8d48-488d-a77e-cf0714f0b5d4","resolution":{"observed_at":"2026-05-15T16:20:11.096750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Spatial channel attention for deep convolutional neural networks","venue":null,"work_id":"9e94d6ca-ab56-425a-bcbc-d0d776f18176","year":2022},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:be1a87f7678e976f01377f2741eee91fdce63d9d46416a1baded1abbb2546308","observation_id":"ff8bfbb5-1e0f-42a0-ac7d-edb455160a50","resolution":{"observed_at":"2026-05-15T16:20:11.137391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"bb50c6de-fd12-4c2c-8f0a-bb6082da0a77","year":2011},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:0c7ba9b9ffc35a2e218e66c1d15a26f0721e531ce74a7d7d824372a91521ee48","observation_id":"7bd6c389-e1bf-4437-975f-f2f50859b642","resolution":{"observed_at":"2026-05-15T16:20:11.239936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1212.0402","last_updated":"2012-12-03T14:45:31Z","snapshot_observed_at":"2026-08-16T21:21:44.768787Z","submitted_at":"2012-12-03T14:45:31Z","title":"UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild","version":1},"cited_work":{"arxiv_id":"1212.0402","doi":"10.48550/arxiv.1212.0402","metadata_source":"pith","pith_arxiv_id":"1212.0402","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild","venue":"cs.CV","work_id":"5dfb46e7-e952-409d-a3c7-ba7f20aebad6","year":2012},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"cited_paper":"/paper/1212.0402","citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:bac8b1c99fc7b399896e2700797439ac1a3fe386c7f82ac2bdc978e21f009bf7","observation_id":"919afd40-6f69-4606-8c1c-824a6f949167","resolution":{"observed_at":"2026-05-14T20:19:27.147154Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset","venue":null,"work_id":"4a4a94ec-b71d-45f1-940b-cb653955040a","year":2017},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:851fbe5cca895f0bc44b666101c9ed17f96f866ceddb7367ce14280a4f15596d","observation_id":"c1760e3a-912f-406b-80bf-41500d810923","resolution":{"observed_at":"2026-05-15T16:20:11.221621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"The” something something","venue":null,"work_id":"a9917b42-09da-4016-8e2c-23d8450dd7ee","year":2017},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:4e41dc3d166107d457c9baaa503ab1a2eb540cc11c8ce3e57787496a2cc62802","observation_id":"d0518614-8494-4e7f-b0fe-cd65d5765862","resolution":{"observed_at":"2026-05-15T16:20:11.251492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Few-shot action recognition with permutation-invariant attention","venue":null,"work_id":"2c158a77-a806-4d39-b658-97946e21c315","year":2020},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:600934428dc474ca7d8bf71a21d6f6a57920ead6c90747591fe99de711f2d790","observation_id":"3718b6fd-a7d2-4918-8504-3d7ec66fa8b1","resolution":{"observed_at":"2026-05-15T16:20:11.208027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Deep residual learning for image recognition","venue":null,"work_id":"6a454b1a-0675-4085-8eec-3ea139afc43f","year":2016},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:f7537ea47d25f676ca5a887f35952461453e55ce756289890a9ff73886b07427","observation_id":"243ebf9f-d5bf-4b7f-8971-829d91cdf1b4","resolution":{"observed_at":"2026-05-15T16:20:11.193275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":"069530e4-25af-460a-a536-f90829c4552b","year":2021},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:566921cf9cc7e4ca60ec412e31e7cd5e9dfdc263957125387008d0c8bf891261","observation_id":"107dd68d-9709-4770-9358-c475846c7fd3","resolution":{"observed_at":"2026-05-15T16:20:11.235564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":"2303.08774","doi":"10.1002/tea.20265","metadata_source":"pith","pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GPT-4 Technical Report","venue":"cs.CL","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","year":2023},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:071bbe1c907463eabfb3b47117822f5fd7b464fba06e922d0cd0d4da90a6567a","observation_id":"4b887a33-0557-4379-9e9d-c6b6c3e6a3cf","resolution":{"observed_at":"2026-05-14T20:19:27.142122Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:163d0cf3c82cc3cb1720e34165c46a45f8b8e38338fba2ab1dd2637b4079a6c7","observation_id":"e7b9e1fd-59b8-40b4-add0-997b4e04a273","resolution":{"observed_at":"2026-05-14T20:19:27.164305Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Hello gpt-4o","venue":null,"work_id":"df90e281-b09d-4ccf-a24b-b90a1d750123","year":2024},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:a0c8494e9bc2c5e952b9050caaae83b06ec09a272034409d6a197756b6d63e60","observation_id":"8cd6d79c-edb5-49e6-b352-f1f2b4d34457","resolution":{"observed_at":"2026-05-15T16:20:11.297302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Gemini 2.5: Our most intelligent ai model","venue":null,"work_id":"a0bded83-c1e1-4018-8b10-04fb254dc8dd","year":2025},"citing_paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-14T20:18:36.505585Z"},"links":{"citing_paper":"/paper/2605.13202"},"observation_digest":"sha256:959191fcc4243372f1b5bd2022c07df7ce390005b08c8edd89f1a0a02e92d827","observation_id":"d5ccf48b-b331-45f3-9f71-5ff448d77e7d","resolution":{"observed_at":"2026-05-15T16:20:11.147695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.13202","last_updated":"2026-05-13T08:54:38Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T08:29:49.793347Z","submitted_at":"2026-05-13T08:54:38Z","title":"STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition"},"reference_resolution":{"displayed":72,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":9,"verified_fuzzy":63},"total_outbound_references":72},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2605.13202."}