{"as_of":"2026-08-13T04:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1ed64e5135186b9c37982a6e4724304e798c08285fdcccfb428268d229d632bc","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T18:34:03.121963Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2501.13950/citation-record","integrity":"/paper/2501.13950/integrity","json":"/paper/2501.13950/citation-record.json","paper":"/paper/2501.13950"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:02.945024Z","title":"Flamingo: a visual language model for few-shot learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.945024Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:60c6c1071dc7dd2bca564cbc6226085804d02c47ec0ddf742fe560a45f84130f","observation_id":"a7c0c13d-91c6-4011-a787-3ba120abb41d","resolution":{"observed_at":"2026-08-10T18:34:02.945024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08521","last_updated":"2024-07-15T19:40:44Z","snapshot_observed_at":"2026-08-12T23:24:11.148821Z","submitted_at":"2024-07-11T14:09:42Z","title":"Emergent Visual-Semantic Hierarchies in Image-Text Representations","version":2},"cited_work":{"arxiv_id":"2407.08521","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.08521","snapshot_observed_at":"2026-08-10T18:34:03.275557Z","title":"Emergent Visual-Semantic Hierarchies in Image-Text Representations","venue":"cs.CV","work_id":"8e21d740-8781-4f18-9842-826bae6e18e3","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.948860Z"},"links":{"cited_paper":"/paper/2407.08521","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:104b2a15a62204f3d2f9f5bda7a566abe698bce31ee4dcfdaad15e0084ffd949","observation_id":"9d63d09a-614d-4666-93b2-3058b2fc865c","resolution":{"observed_at":"2026-08-10T18:34:03.279642Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.699934Z","title":"Emerg- ing properties in self-supervised vision transformers","venue":null,"work_id":"57e58cf6-49c4-408e-b6db-a22b2d965b4a","year":2021},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.952871Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:37cc0c220494ac3f120bc369deed32744b0237bb915bd4cb2970b3444c1ddb0c","observation_id":"2a94395f-4811-4799-89ef-e93c85bfeb35","resolution":{"observed_at":"2026-08-10T18:34:03.704402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06310","last_updated":"2024-11-18T19:03:35Z","snapshot_observed_at":"2026-08-13T02:15:41.799066Z","submitted_at":"2023-04-27T03:41:15Z","title":"SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition","version":4},"cited_work":{"arxiv_id":"2305.06310","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.06310","snapshot_observed_at":"2026-08-10T18:34:03.257285Z","title":"SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition","venue":"cs.CV","work_id":"c7bf6464-c09c-465d-be37-676d11038fee","year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.956529Z"},"links":{"cited_paper":"/paper/2305.06310","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:2617960a174eb55ea56a3b9d479749b470b106e193635c2a7248e1b2f18d25f1","observation_id":"9fc26dcb-e472-4646-9513-e48dec1e2cce","resolution":{"observed_at":"2026-08-10T18:34:03.263689Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.689481Z","title":"Spartan: Self-supervised spatiotemporal transformers ap- proach to group activity recognition","venue":null,"work_id":"96721459-ba25-4df1-9345-aad51e1dfb93","year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.960315Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:c271a40e7ca42a7aee8dd209ace767fafcb3d0d9b38cfcc210bec178e48f576e","observation_id":"81990c7e-09e8-455e-8e4b-969eaa2a585f","resolution":{"observed_at":"2026-08-10T18:34:03.693144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.679034Z","title":"Advanced deep learning techniques for tobacco usage assessment in tiktok videos","venue":null,"work_id":"3c896b18-368c-4cd0-a7ef-81d967515770","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.963892Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:289ddb7c3139b46bf9f8df0c18d6a741e2f1bb9bb6a82cc2c52914da2cbba3b1","observation_id":"7b87733a-98fa-494b-8424-0184f42fda28","resolution":{"observed_at":"2026-08-10T18:34:03.682525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.666979Z","title":"React: Recognize every action everywhere all at once","venue":null,"work_id":"32d2a60f-37cb-484e-8bee-5f83710e0ba5","year":null},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.967310Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:5bf3137a771ca9dc1ea091ea72f35c3f226b98b8e1a8875bdf70cd30eb71c157","observation_id":"3859bf85-13cd-4c5e-b3e3-f35d57f3bf08","resolution":{"observed_at":"2026-08-10T18:34:03.670808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.655201Z","title":"Public health advocacy dataset: A dataset of tobacco usage videos from social media","venue":null,"work_id":"0b636b80-c6ce-4e21-933a-81ad25da7a7e","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.970711Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:497e5262c59ef6a297425887f142dbe25defe4334424bb009d9059c1381c2b79","observation_id":"25f40067-c5e6-4114-a14f-df0906165ccc","resolution":{"observed_at":"2026-08-10T18:34:03.659014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.643149Z","title":"Hatt- flow: Hierarchical attention-flow mechanism for group- activity scene graph generation in videos","venue":null,"work_id":"f89641b5-38b0-4462-8e90-f7ae52d63d40","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.974151Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:83fb37046f709797568a0a335f39ba472e11ea6badfacd6bd86232fc7edf7819","observation_id":"5f8593aa-c03c-4ff0-871e-5d59ef119492","resolution":{"observed_at":"2026-08-10T18:34:03.647378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.631959Z","title":"Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning","venue":null,"work_id":"f41dd84c-8d74-4ee7-8673-845b8bf93cd3","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.977029Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:6de093a91d91a8b90f68dfe4c0477a5094f39a6e8f88324effee2c71ac2b3653","observation_id":"116ed0e8-4449-41d4-93c8-b21a17e0ccf3","resolution":{"observed_at":"2026-08-10T18:34:03.635503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.621446Z","title":"Improved baselines with momentum contrastive learning","venue":null,"work_id":"81c0c066-1e68-4f2c-8b5f-ddbd1bc1c202","year":null},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.980061Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:85fa28ea9958b2d228cc4ef900d442489b1646abefdc332f4df5f2abbad222e7","observation_id":"9aa9ea00-ecf1-4e10-9c94-78508b8d9d32","resolution":{"observed_at":"2026-08-10T18:34:03.625061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-10T18:34:02.983513Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.983513Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:55a877b913c9cb72825295e5652e58e265de0f734abbc267c2399e460f9fa4f4","observation_id":"b8f89a8d-76e2-421a-9030-d87d2fb2fc5a","resolution":{"observed_at":"2026-08-10T18:34:02.983513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T02:40:23.887636Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-10T18:34:02.986947Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.986947Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:79e29d0cae1a6cabaec4ae6a7a88e604b1e00c60dd52568725788e932aebf5de","observation_id":"e390c17a-5a0b-46d4-a789-c991da7e7514","resolution":{"observed_at":"2026-08-10T18:34:02.986947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.11227","last_updated":"2021-04-22T17:59:45Z","snapshot_observed_at":"2026-08-10T18:08:12.282553Z","submitted_at":"2021-04-22T17:59:45Z","title":"Multiscale Vision Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.11227","snapshot_observed_at":"2026-08-10T18:34:02.990357Z","title":"Multiscale vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.990357Z"},"links":{"cited_paper":"/paper/2104.11227","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:a692a2d668d7a2dce2c0d8bac7fc0c2574088119bc5612ea50084d861a68ce69","observation_id":"8526b7d1-b4fe-40b6-944d-7d1e3657a067","resolution":{"observed_at":"2026-08-10T18:34:02.990357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14150","last_updated":"2024-06-20T09:44:53Z","snapshot_observed_at":"2026-08-12T23:38:33.398652Z","submitted_at":"2024-06-20T09:44:53Z","title":"Multi-modal Transfer Learning between Biological Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14150","snapshot_observed_at":"2026-08-10T18:34:02.994040Z","title":"Multi-modal transfer learning between biological foun- dation models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.994040Z"},"links":{"cited_paper":"/paper/2406.14150","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:33e3b6c850c9b1410e5e47f2a076e2a27809abee4c03a47e4408613de0dfb070","observation_id":"d8238825-ca2c-48c0-8e0e-a1f9f33ec958","resolution":{"observed_at":"2026-08-10T18:34:02.994040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:02.997769Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:02.997769Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:6a85838c9beb13f802ab973eb0540748e112ad5732346199c67dd978a29aeb0c","observation_id":"97e3b5a8-d6ec-4020-b775-4700c47835dc","resolution":{"observed_at":"2026-08-10T18:34:02.997769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.001325Z","title":"Momentum contrast for unsupervised visual rep- resentation learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.001325Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:ab6a895fb44145422b259835c8972cac5be804d4f0ddbf49408050aad475977c","observation_id":"b405ab01-bcb8-4331-bc93-74003b2a3dfe","resolution":{"observed_at":"2026-08-10T18:34:03.001325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.596879Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":"e08a1e0c-41dc-4b25-b8ba-87b2a6fcb670","year":2022},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.004801Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:3d4d36b0d3ee123d34d7b191daceaa7d26d3b1c4bc52fd48beff68cf3b54d019","observation_id":"4391ccf5-b7d0-4d20-901b-645abe8ef8ff","resolution":{"observed_at":"2026-08-10T18:34:03.600371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.008398Z","title":"Scaling up visual and vision-language representa- tion learning with noisy text supervision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.008398Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:93d0d0b1b1c7ad68776f70c2d5af6f6840f9802ee2540fe0acc4d28d1df99d2b","observation_id":"341168e1-61a2-49dd-ace7-abbcbf83035b","resolution":{"observed_at":"2026-08-10T18:34:03.008398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.580357Z","title":"Mdetr- modulated detection for end-to-end multi-modal understand- ing","venue":null,"work_id":"d7d2f6f8-5451-46d7-ad10-0459185fd1b3","year":2021},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.012478Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:68fa6fa0341d0688d533c18237fb361cda5c3fce2ae6555426c82b446e9ac8a1","observation_id":"fddc1b31-cfae-4b04-bf4a-e28204189a2c","resolution":{"observed_at":"2026-08-10T18:34:03.583724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17431","last_updated":"2024-03-29T05:51:53Z","snapshot_observed_at":"2026-08-12T03:38:20.215685Z","submitted_at":"2023-11-29T08:21:42Z","title":"Grounding Foundation Models through Federated Transfer Learning: A General Framework","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17431","snapshot_observed_at":"2026-08-10T18:34:03.016862Z","title":"Grounding foundation models through federated transfer learning: A general framework","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.016862Z"},"links":{"cited_paper":"/paper/2311.17431","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:cb9ff970db013bb8aa70aa4a7ae3c4be77e3320313b0df2814f171407332590b","observation_id":"a0fa5924-f71a-4383-8053-41854490c971","resolution":{"observed_at":"2026-08-10T18:34:03.016862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.568637Z","title":"Ma- chine learning models of tobacco susceptibility and current use among adolescents from 97 countries in the global youth tobacco survey, 2013-2017","venue":null,"work_id":"848a17cd-fee7-41f1-b88e-b551458e407f","year":2013},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.020696Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:858c859cac6c36176b5a88952507f83b04fc255cb5564a1b87f1b4383caa37a9","observation_id":"0ead8066-5eae-4576-a78c-d53337798348","resolution":{"observed_at":"2026-08-10T18:34:03.573030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.556197Z","title":"Understanding e-cigarette con- tent and promotion on youtube through machine learning","venue":null,"work_id":"7aa66181-221b-4b85-884e-c5c02347fb67","year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.024512Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:dff42d0b783ed8c657f1cca140c79e2971c5e4fe37a547d73ba18d9257478f9a","observation_id":"8ccdda3a-8e04-4d9a-9f06-dd4f4204d793","resolution":{"observed_at":"2026-08-10T18:34:03.560568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.542958Z","title":"A multimodal deep learning architecture for smoking detec- tion with a small data approach","venue":null,"work_id":"a025444b-9907-44a6-9b7a-85075b02c3d6","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.027721Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:0d464d9d889d9f4b7855625169137d3d848bb746db37994fa0b730250439087a","observation_id":"30409008-7cd8-4951-a280-b22dd2d609cb","resolution":{"observed_at":"2026-08-10T18:34:03.547222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.030975Z","title":"Visual instruction tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.030975Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:dc68d1e63c7359e74b1bbfca0ee53f88fbb8abd69a864e11ba8950c6074e041c","observation_id":"752ad692-ac68-4beb-a4b0-71591de01f63","resolution":{"observed_at":"2026-08-10T18:34:03.030975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-09T20:34:52.923500Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-10T18:34:03.034308Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.034308Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:49627a75ff5b4a8625dc762fabbefcb254027e51d29bb3f7c312ebbfe77bac25","observation_id":"223046a2-d196-4465-ba95-75fbbee8e854","resolution":{"observed_at":"2026-08-10T18:34:03.034308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1608.03983","last_updated":"2017-05-03T16:28:09Z","snapshot_observed_at":"2026-07-06T05:06:55.589962Z","submitted_at":"2016-08-13T13:46:05Z","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.03983","snapshot_observed_at":"2026-08-10T18:34:03.037520Z","title":"Sgdr: Stochas- tic gradient descent with warm restarts","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.037520Z"},"links":{"cited_paper":"/paper/1608.03983","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:2ab839123600649ffe8e398f97f274fb39f9522c4f667406d40e160594528395","observation_id":"4bc95553-7693-4c16-a108-07a43532bb90","resolution":{"observed_at":"2026-08-10T18:34:03.037520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.522559Z","title":"Visual relationship detection with language priors","venue":null,"work_id":"12c8d93b-c300-46cd-a470-825ccea6eed8","year":2016},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.041129Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:1721b1f4b604bd12fd847a95a1d8844cf8307b22c75d287ed2a3300ac2952cfc","observation_id":"7048106b-2996-4f99-9222-c0741284d58a","resolution":{"observed_at":"2026-08-10T18:34:03.526809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.511028Z","title":"Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks","venue":null,"work_id":"7ee0a094-1fce-42bf-bce9-7514e81c8a07","year":2019},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.044086Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:86e608bcdcf4470e69d8c8249cdf637d18807a87655bd91fd3b24b02ced7bb3c","observation_id":"65315a40-f3a3-4017-972a-88b02130db3f","resolution":{"observed_at":"2026-08-10T18:34:03.515214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09484","last_updated":"2023-07-21T05:13:55Z","snapshot_observed_at":"2026-08-12T21:18:53.136076Z","submitted_at":"2023-06-06T12:45:15Z","title":"MolFM: A Multimodal Molecular Foundation Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09484","snapshot_observed_at":"2026-08-10T18:34:03.047178Z","title":"Molfm: A multimodal molecular foundation model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.047178Z"},"links":{"cited_paper":"/paper/2307.09484","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:e4fa68d188b30bcb89815d6c782c1c006f80ba2f3ef7ae82c2446721e67438e3","observation_id":"85596e3e-a622-40fe-a641-22d536ac4e0c","resolution":{"observed_at":"2026-08-10T18:34:03.047178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.498960Z","title":"A scalable hierarchical distributed language model","venue":null,"work_id":"837c5aaa-6366-4325-b074-15ab6375667e","year":2008},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.050887Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:ed0cda252b39d7320c940fb5c738c56b23cb4869c34cd4bfd331d1e50fd40e8d","observation_id":"ee6abc13-d194-4610-85ee-acb8a95bf8f3","resolution":{"observed_at":"2026-08-10T18:34:03.503184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.488658Z","title":"Influence of user profile attributes on e-cigarette– related searches on youtube: Machine learning clustering and classification","venue":null,"work_id":"6318b611-bc2a-4ade-95bc-61e84cba8a29","year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.054271Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:90c321e2190ea04fd4973dc9bd3abf6159fa6b59a509ad24cd7f3037fb6d420b","observation_id":"408435b9-1d42-4262-80f8-a4016d26446c","resolution":{"observed_at":"2026-08-10T18:34:03.492254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.477118Z","title":"Using Computer Vision to Detect E-cigarette Con- tent in TikTok Videos","venue":null,"work_id":"b11dea5e-e472-4601-88cc-84a7762b655c","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.057484Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:778bfa121e4c6914ca2c2050d9479c563e074ebc6970ff03d04b94e8e343524d","observation_id":"8f5a78e1-ae74-4b24-bf3e-a4131a7f0c9d","resolution":{"observed_at":"2026-08-10T18:34:03.481187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.466089Z","title":"Insect- foundation: A foundation model and large-scale 1m dataset for visual insect understanding","venue":null,"work_id":"d492e069-d50d-4707-b0de-1caa3b157e3b","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.060790Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:329ed914d804efdc63ffab2e276eca1a99c3d9333cecabc8c1962b3f4f54e92d","observation_id":"87e942bf-5732-4297-9664-f4a35c686b4f","resolution":{"observed_at":"2026-08-10T18:34:03.469874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.454891Z","title":"Type-to-track: Retrieve any object via prompt-based track- ing","venue":null,"work_id":"d4cbec73-4289-4fc2-90b0-5fa691a3d864","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.063965Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:248eaa72ba60d8226b81e3e8870d4bcc41e1d9f9208eabb274ad37a2b2ba5f10","observation_id":"994be3a8-c1f6-4cff-840e-1f55fa337f9d","resolution":{"observed_at":"2026-08-10T18:34:03.458758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.443141Z","title":"Classification of lapses in smokers attempting to stop: A su- pervised machine learning approach using data from a pop- ular smoking cessation smartphone app","venue":null,"work_id":"0b02058c-c10f-42fb-929b-438f69970c12","year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.067235Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:81889ed69c593e985782d0d92839eefe6b5c085e4814c4a0552c8089be4e7087","observation_id":"1d5d37ed-699d-4b66-a8b0-6dc90cf0e0d0","resolution":{"observed_at":"2026-08-10T18:34:03.447359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.431763Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"e94d252d-29d3-49d9-b2c7-e6440319749d","year":2021},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.070429Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:cf94f21cd09bc2b6f189d7ea37a9bab578713ce7d18f87612bc260abaafb6546","observation_id":"d87487a7-2b56-469c-a567-d296ace6e138","resolution":{"observed_at":"2026-08-10T18:34:03.435492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.073531Z","title":"Efficientnet: Rethinking model scaling for convolutional neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.073531Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:0459d18e52399dc7953aca824dd3d3f953968af2a5edf95de4c4f22a554d4d3e","observation_id":"8f0368ec-087f-47b7-9e3c-1ec75dd88035","resolution":{"observed_at":"2026-08-10T18:34:03.073531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.413688Z","title":"Learning to compose dynamic tree structures for visual contexts","venue":null,"work_id":"18e7f653-fc88-42b8-a9f9-8a78922006d1","year":2019},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.076825Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:7a711b6f8de348c34ff562ff61ddaedfdf323833e6d278741566bd4c367d7589","observation_id":"5a5e4d46-1537-4623-b390-909f58cfac83","resolution":{"observed_at":"2026-08-10T18:34:03.417408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.402813Z","title":"Scalable Surveil- lance of E-Cigarette Products on Instagram and TikTok Us- ing Computer Vision","venue":null,"work_id":"3c42dd38-2b73-4bc2-9b22-65d34ffc6480","year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.079685Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:636dd366060cde88b8915d4bf39a8777624d73153308d0c9029c027a4228ce10","observation_id":"f86defb6-5c22-4831-abb4-34bb12b473d5","resolution":{"observed_at":"2026-08-10T18:34:03.406615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.082745Z","title":"Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.082745Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:42e0f1678e55f26638d77ddcf06c0d5206baedb5919fd865461d70552677b32b","observation_id":"f5497276-6700-496a-94cd-2f6c42cd2630","resolution":{"observed_at":"2026-08-10T18:34:03.082745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.08472","last_updated":"2021-09-17T11:21:34Z","snapshot_observed_at":"2026-08-12T22:18:23.713183Z","submitted_at":"2021-09-17T11:21:34Z","title":"ActionCLIP: A New Paradigm for Video Action Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.08472","snapshot_observed_at":"2026-08-10T18:34:03.086106Z","title":"Actionclip: A new paradigm for video action recognition","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.086106Z"},"links":{"cited_paper":"/paper/2109.08472","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:3142c943231269f464168c4fbd0ddd3c57be6cf7194a3892f6f87cf780f089d4","observation_id":"dcfd8532-822a-4d3d-835c-08201d246b8e","resolution":{"observed_at":"2026-08-10T18:34:03.086106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.385094Z","title":"Ip102: A large-scale benchmark dataset for insect pest recognition","venue":null,"work_id":"a88095bc-063c-45cd-91a2-05c4dd33489d","year":2019},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.089438Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:ee3973dbeb1042e0213cf75de9a88ccbb15205b6d2bac49e26f679ad249011e0","observation_id":"75473761-0f3e-421b-a882-d91b63efa08e","resolution":{"observed_at":"2026-08-10T18:34:03.388736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.373307Z","title":"mplug-2: A modularized multi-modal foundation model across text, image and video","venue":null,"work_id":"a9f9f93f-a40a-4eb6-9bd4-c52e18c66865","year":null},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.092763Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:3b1128118821d8c32beb4990a0951f35bb514dbf8fcdf6f9b51dd107c0cd9c2b","observation_id":"43f70c82-cb16-4ef1-9bfa-9fe7258ff218","resolution":{"observed_at":"2026-08-10T18:34:03.377434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.358652Z","title":"Seed the views: Hi- erarchical semantic alignment for contrastive representation learning","venue":null,"work_id":"4d4c37a0-ba0b-4b91-99a3-7f72f6a85aa8","year":2023},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.096591Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:e08038ca21a4d1dd8c4a72b295a53c58c755125ce57adee36be61ffbf3427f7a","observation_id":"7ac38fa1-ef71-4c70-9d1f-4a8a32d34e6f","resolution":{"observed_at":"2026-08-10T18:34:03.365981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.346900Z","title":"Linguistic structures as weak supervision for visual scene graph generation","venue":null,"work_id":"433d034e-3a05-4a0d-b08c-6fd0c9b376ae","year":2021},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.099803Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:ce9336b62854c492fcb15b1be94fd7597a7a6100972a44c8203c12585702336a","observation_id":"8b6c8476-dafc-44c7-9f7e-1ea3e3ac05e3","resolution":{"observed_at":"2026-08-10T18:34:03.350939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01917","last_updated":"2022-06-14T00:48:04Z","snapshot_observed_at":"2026-08-10T15:05:25.558818Z","submitted_at":"2022-05-04T07:01:14Z","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01917","snapshot_observed_at":"2026-08-10T18:34:03.103176Z","title":"Coca: Contrastive captioners are image-text foundation models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.103176Z"},"links":{"cited_paper":"/paper/2205.01917","citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:129c6c03b552fda7293d736273f5899e16f2e3b575b2e4ea740243c0f1e55407","observation_id":"00621a85-4750-4ec2-b6dd-17b41d3a100c","resolution":{"observed_at":"2026-08-10T18:34:03.103176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.335085Z","title":"Bridging knowledge graphs to generate scene graphs","venue":null,"work_id":"ba57d60f-8220-47f0-a7f3-0b42abdf4fd5","year":2020},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.107134Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:88d1be9953aaab3c63f3aa01fb5e5154158572c159d813e27823b60caffbed6c","observation_id":"95c2d998-fa6b-4e93-9474-89157a90223f","resolution":{"observed_at":"2026-08-10T18:34:03.338915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.322693Z","title":"Learning visual commonsense for robust scene graph 10 generation","venue":null,"work_id":"3b8d3bb0-7d7f-4781-b15d-46cdbe7b639f","year":2020},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.110705Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:6f2e79e493f88ff187926039d26a024ca67eb7dc82703e59982182759fef6dd4","observation_id":"c4245b08-110a-40d3-a258-07289fe49c48","resolution":{"observed_at":"2026-08-10T18:34:03.326452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.311353Z","title":"Learning human action recognition representations without real humans","venue":null,"work_id":"f5675a96-273a-4b55-8994-9b485f6b8714","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.114260Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:c77b89b2d868663fbff0e5fb7c25b3337511b487d85fe387b143fcc67e056b55","observation_id":"a05f449c-f9c2-4085-9d58-187e8b0a2ba8","resolution":{"observed_at":"2026-08-10T18:34:03.315487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.298546Z","title":"Learning to generate scene graph from natural language supervision","venue":null,"work_id":"88b38bf3-c4fd-4cf3-aef9-70bbce1cdddf","year":2021},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.118151Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:3b3eb3dd6da213f03409f3a0fab4f9744131d1c0649dbee60191d9017130feb7","observation_id":"82df4489-1209-4fb5-a88c-b7045ea77023","resolution":{"observed_at":"2026-08-10T18:34:03.302331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:34:03.287074Z","title":"MiniGPT-4: Enhancing vision-language understanding with advanced large language models","venue":null,"work_id":"0b31bab5-af8b-4ea5-b8be-9f7c5560a038","year":2024},"citing_paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T18:34:03.121963Z"},"links":{"citing_paper":"/paper/2501.13950"},"observation_digest":"sha256:919bbaa6037fde457624e95708b7c87dfca93ea44a8eb95371aa5a3e0d88edd5","observation_id":"f2359b60-70f1-462a-a011-de1b45cdc41c","resolution":{"observed_at":"2026-08-10T18:34:03.290766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.13950","last_updated":"2025-01-20T02:55:46Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T03:38:54.239154Z","submitted_at":"2025-01-20T02:55:46Z","title":"DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":2,"verified_fuzzy":33},"total_outbound_references":52},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2501.13950."}