{"as_of":"2026-08-18T10:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8ae8989f2b6053c736533a676cd5a449605044ee326d16558204a44424fdbbbe","coverage":[{"denominator":98,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":98,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T12:41:21.124880Z","state":"measured"},{"denominator":98,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":98,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2502.02307/citation-record","integrity":"/paper/2502.02307/integrity","json":"/paper/2502.02307/citation-record.json","paper":"/paper/2502.02307"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:41:20.771881Z","title":"L2cs-net: Fine-grained gaze estimation in unconstrained environments","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.771881Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:b838348992bf5821b4ada8f30b936df9271bddfb85b95529794425f61510dd09","observation_id":"04ca40f9-5673-4acb-9ae0-0d947f42d3f6","resolution":{"observed_at":"2026-08-09T12:41:20.771881Z","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":"2026-08-09T12:41:20.776265Z","title":"Non-intrusive gaze tracking using artificial neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.776265Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:2978e4047f924f7aa6749288ec63eeaed4eb7b8bfa38d1f0cd690aa8680e2e70","observation_id":"771694dd-f738-42c2-a77a-3c2a45991c77","resolution":{"observed_at":"2026-08-09T12:41:20.776265Z","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":"2026-08-09T12:41:20.779831Z","title":"From feature to gaze: A generaliz- able replacement of linear layer for gaze estimation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.779831Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:e2e6b0089608e2e808732c8c46b21048bc5fdcd592179f2e3bcfdf9d93d5ef64","observation_id":"f88c7e9f-c71f-4a89-af62-824b28c0de47","resolution":{"observed_at":"2026-08-09T12:41:20.779831Z","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":"2026-08-09T12:41:20.783891Z","title":"Unsupervised gaze representation learning from multi-view face images","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.783891Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:4aa9a04de6c6934cabbdbae38005d0cb435c214cf53f7aac39e5504b92386cce","observation_id":"918aef13-b0f6-4cb9-bcc1-e1810cf2d817","resolution":{"observed_at":"2026-08-09T12:41:20.783891Z","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":"2026-08-09T12:41:20.786895Z","title":"Gaze-based intention estimation: princi- ples, methodologies, and applications in hri","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.786895Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:ee478ca7f436f4beded14b3947a5f5c5b9cde9610327753b9566d0cfcf611c79","observation_id":"3a10488a-bb70-4d9d-aec5-38eafe9db36c","resolution":{"observed_at":"2026-08-09T12:41:20.786895Z","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":"2026-08-09T12:41:20.791110Z","title":"Synthetic faces high quality - text 2 image (sfhq-t2i) dataset, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.791110Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:a4865f1d9381f7c38c2dad540529ab4ad0e372d9a5649cdc1f9baee51bcc1b68","observation_id":"9ad333a8-e553-4894-8236-cbc867f123eb","resolution":{"observed_at":"2026-08-09T12:41:20.791110Z","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":"2026-08-09T12:41:20.795125Z","title":"How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.795125Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:7c95135343626c6df9e8ed0082a5fec6129cbe87c8318908fc655f3bee58ba1e","observation_id":"702a0751-480e-4251-a5d9-b96251b58229","resolution":{"observed_at":"2026-08-09T12:41:20.795125Z","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":"2026-08-09T12:41:20.799089Z","title":"Marlin: Masked autoencoder for facial video repre- sentation learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.799089Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:2b97c0d7dcacbe0fb57c55b1586707d6a461d78d4ceac0a944b6f11cc138f6b1","observation_id":"feda408f-4dfb-4b46-85b6-2c63325fd362","resolution":{"observed_at":"2026-08-09T12:41:20.799089Z","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":"2026-08-09T12:41:20.803288Z","title":"Vggface2: A dataset for recognising faces across pose and age","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.803288Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:22dd9b9499a3241813c1c90e18c1b3e5767e5041886882d3ca9da960977a349e","observation_id":"89889062-d576-4a0e-9c51-2e3750a801a9","resolution":{"observed_at":"2026-08-09T12:41:20.803288Z","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":"2026-08-09T12:41:20.807337Z","title":"Emerg- ing properties in self-supervised vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.807337Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:60af2780056646ddb9758f41e8053efc411b90e2e32c61b17264bd3dd0ef6c69","observation_id":"538cbe01-6017-4848-9be9-0c51ff3aad32","resolution":{"observed_at":"2026-08-09T12:41:20.807337Z","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":"2026-08-09T12:41:20.811547Z","title":"An empiri- cal study of training self-supervised vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.811547Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:5e574fbacde074f3f379c90f0f23fd8dc30a53dc18fe0d70e15306a5a315b29a","observation_id":"fced9cb0-1239-4eb4-9d6e-78ca7bfb0788","resolution":{"observed_at":"2026-08-09T12:41:20.811547Z","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":"2026-08-09T12:41:20.815280Z","title":"Gaze estimation using trans- former","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.815280Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:1d5f398de91fbf8e0b06a74f8637b0e239d539701b261165c3f69b141002d6f0","observation_id":"a3239a1c-148d-4957-8d13-702ad57fa7a0","resolution":{"observed_at":"2026-08-09T12:41:20.815280Z","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":"2026-08-09T12:41:20.819089Z","title":"Puregaze: Purify- ing gaze feature for generalizable gaze estimation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.819089Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:a3e7ce61fb2b418f514cc59e5ab0365c800625694e5c8ea51f4f2b621754f545","observation_id":"cfdef371-b148-4693-b148-25ebd394fd70","resolution":{"observed_at":"2026-08-09T12:41:20.819089Z","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":"2026-08-09T12:41:20.822656Z","title":"Appearance-based gaze estimation with deep learning: A re- view and benchmark","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.822656Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:14204dd9af6a3330d149d4ad142ba29778bcbcd1e34a8beb9d24169e97356994","observation_id":"b07deda4-2cac-48b2-91fd-9c8d62239d43","resolution":{"observed_at":"2026-08-09T12:41:20.822656Z","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":"2026-08-09T12:41:20.826398Z","title":"Detecting attended visual targets in video","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.826398Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:763197358e9859fb8fb9824b6d2586d9e6034b19c0b85890bd31056f0cc7da6b","observation_id":"8cea9901-3ba3-4cd9-a306-20004ed90189","resolution":{"observed_at":"2026-08-09T12:41:20.826398Z","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":"2026-08-09T12:41:20.830125Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.830125Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:b9cb1a5dc10cd7b3cedac79f76a2cc291ba3e863f9bf9a850409fd0de84bd1c6","observation_id":"321dd140-17e9-4fa3-822d-fe8b5793aab6","resolution":{"observed_at":"2026-08-09T12:41:20.830125Z","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":"2026-08-09T12:41:20.833650Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.833650Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:d99ba9debc0e8f1e2d8a9dfcd4e009d1915ac273af023de1250890ed159ace47","observation_id":"1af19e61-5235-4db9-85ea-e4e9e4caebdf","resolution":{"observed_at":"2026-08-09T12:41:20.833650Z","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":"2026-08-09T12:41:20.837593Z","title":"Corrupted image modeling for self-supervised vi- sual pre-training","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.837593Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:8b41c5110fc1b468fb04627025108be5538c43f5147b3fded5b5c634fb99ce3d","observation_id":"bbd5ee4f-f16f-4f91-9592-c3b0ebc8e0e9","resolution":{"observed_at":"2026-08-09T12:41:20.837593Z","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":"2026-08-09T12:41:21.840033Z","title":"Eva: Exploring the limits of masked visual representa- tion learning at scale","venue":null,"work_id":"ac38370b-4f44-427a-b547-8a14b0b0d0f3","year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.841459Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:a76ef34545dd338f9df2132a90672ee2125f941d9465645884525a7587846b6d","observation_id":"4139a793-4645-4d2b-91f5-e9a1fe831b64","resolution":{"observed_at":"2026-08-09T12:41:21.842630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.831610Z","title":"Rt- gene: Real-time eye gaze estimation in natural environments","venue":null,"work_id":"ac23ec3a-0620-43fd-a4e0-7d3f513dc27e","year":2018},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.845478Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:a1f2503b19a2cc57ae41df0c8f6f08290a974ba914bcc25ad9bf135e7414a01d","observation_id":"71a722a1-6dfe-4a4b-b788-6dcedbb00da7","resolution":{"observed_at":"2026-08-09T12:41:21.835191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:20.849321Z","title":"Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.849321Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:e7d43a425f46140e6015925e3a491e6e89158635605e5e0ab2b4b2918d78922b","observation_id":"8d38b4d5-1bf3-4ccc-b612-5e1b0871bd18","resolution":{"observed_at":"2026-08-09T12:41:20.849321Z","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":"2026-08-09T12:41:21.817972Z","title":"Eyediap: A database for the development and evaluation of gaze estimation algorithms from rgb and rgb-d cameras","venue":null,"work_id":"91eb28b4-dbc3-4613-a7b4-3773bac771d4","year":2014},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.853289Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:c3d53953a08758618db60611d82f038ddbe9df925e027648841ef096b8a301d0","observation_id":"22970c6d-1f06-47bb-9dd3-e872011dbfbc","resolution":{"observed_at":"2026-08-09T12:41:21.821218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.809298Z","title":"Self-supervised facial rep- resentation learning with facial region awareness","venue":null,"work_id":"6f6aa9a0-e8d6-4c6b-8241-b014322c6a8e","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.857034Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:3ec879b2fc330bff9d446a5f2add4021201265b1a618edc70793eba5573f1fde","observation_id":"ce20fb36-9117-4fc1-b80e-444605a7379a","resolution":{"observed_at":"2026-08-09T12:41:21.812429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.800644Z","title":"Automatic gaze analysis: A survey of deep learning based approaches","venue":null,"work_id":"4681dc17-835f-4504-a550-25eb50a500e2","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.860902Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:6f6f3e9c7ff16b02d26b10421cd46bfb9b5a2e3f791f0e3aeba96e76102c0289","observation_id":"03770f80-bfce-4746-aa6e-3ddd84f314d2","resolution":{"observed_at":"2026-08-09T12:41:21.803846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.791554Z","title":"End-to-end video gaze estimation via capturing head-face-eye spatial-temporal interaction context","venue":null,"work_id":"2015a62c-dbc2-4087-b62b-a0029d20dab1","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.864688Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:2dbfa33a3d6301ad214305dfac9ec0200572abe837cb6ecf5861d8048eeb1b06","observation_id":"d3593c9f-e392-43fe-a139-82d2f5436b9a","resolution":{"observed_at":"2026-08-09T12:41:21.794728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.782446Z","title":"Application of eye tracking in medicine: A survey, research issues and chal- lenges","venue":null,"work_id":"29f29c3a-a52b-47ae-854a-6a7b86355c92","year":2018},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.868318Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:da171a49f9c0a2b950f98e772dfb6b8265107d47b63893e8aeca5eb0ee5eae5c","observation_id":"dba592e4-439c-449b-a5a2-6f2ca6317950","resolution":{"observed_at":"2026-08-09T12:41:21.785634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:20.872097Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.872097Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:1563f9d0ceab3b0df9864bb28afc15778081d0a0f3c5b12d409fc14aebd87f75","observation_id":"48383720-e05f-4bda-95af-ed5b40c5062e","resolution":{"observed_at":"2026-08-09T12:41:20.872097Z","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":"2026-08-09T12:41:21.769329Z","title":"Momentum contrast for unsupervised visual repre- sentation learning","venue":null,"work_id":"7f402638-dc2c-4ffa-bcca-c0e499db3cf8","year":2020},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.875964Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:570868c94fe710b3b7059a0fd849d93d8576a346a744975afdd6a141b4d53a62","observation_id":"47192867-c0d1-4cbf-a1d3-cd4e6aa871be","resolution":{"observed_at":"2026-08-09T12:41:21.772564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.761027Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":"06910c58-70fd-4a69-8c17-724d800b55fe","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.880004Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:97291656506afad4334deb53405f0632e31b2220c4fa88cd95d627f765a68995","observation_id":"4cd14e39-f355-4c11-b273-adae0a79737b","resolution":{"observed_at":"2026-08-09T12:41:21.764070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.753327Z","title":"Rotation-constrained cross-view feature fusion for multi-view appearance-based gaze estimation","venue":null,"work_id":"669ac8c9-ec2e-4bac-8482-7f965b83b426","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.884011Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:9259d5132c0fa8e1e75040b1a7608760f34cfb1e7fd36b3bb850d4b209c80431","observation_id":"5c08f420-5578-4be2-a0d8-bdc307f9b8b7","resolution":{"observed_at":"2026-08-09T12:41:21.755865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.745981Z","title":"Denoising diffu- sion probabilistic models","venue":null,"work_id":"2f5a041e-0a3f-46a8-8677-cf2b1b43664f","year":2020},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.887824Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:88b44cb8cda5fd22998a3606b5f4517194b45c8204368efbbf0bf2cf9a9aa561","observation_id":"b54e5c18-7d65-4397-8be5-32fc6cf5c7c7","resolution":{"observed_at":"2026-08-09T12:41:21.748381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.739153Z","title":"Masked autoencoders that listen","venue":null,"work_id":"659fc028-c089-435b-8633-423917abd149","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.891581Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:ad882252ff386276b04fd5ab4cd17669e54741d6d66cbc4e987734e2866e36c6","observation_id":"5bfd9ace-8eea-466c-8c79-c8f528197759","resolution":{"observed_at":"2026-08-09T12:41:21.741469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00315","last_updated":"2024-06-29T04:35:08Z","snapshot_observed_at":"2026-08-16T13:38:34.896573Z","submitted_at":"2024-06-29T04:35:08Z","title":"Learning Unsupervised Gaze Representation via Eye Mask Driven Information Bottleneck","version":1},"cited_work":{"arxiv_id":"2407.00315","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.00315","snapshot_observed_at":"2026-08-09T12:41:21.265274Z","title":"Learning Unsupervised Gaze Representation via Eye Mask Driven Information Bottleneck","venue":"cs.CV","work_id":"c5e75121-1c60-4293-af5c-a2b0072717e4","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.895508Z"},"links":{"cited_paper":"/paper/2407.00315","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:19e238d8958c04b15cac17e1197914f2166b4716645e82f8b52ffe76d2258d1c","observation_id":"f674cfa2-4d7a-4c7e-9982-b321663d45ba","resolution":{"observed_at":"2026-08-09T12:41:21.269182Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.732281Z","title":"Redirtrans: Latent-to-latent translation for gaze and head redirection","venue":null,"work_id":"07be993b-9433-46dd-bfdc-5f7ab0a79ec0","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.899637Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:11a29d7d47064d1539355db3aa5220bad2eb235a71b21345a29704754ce36325","observation_id":"eba4ba64-040a-4c77-9ecf-183c1b212894","resolution":{"observed_at":"2026-08-09T12:41:21.734735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.723923Z","title":"Contrastive represen- tation learning for gaze estimation","venue":null,"work_id":"3b42389d-fdf5-40b2-9baa-7fe2bfa7b3d9","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.903328Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:e4a4b7e9e61017f8c310c5eadb50688c8233837a0e6cd2248b3c1dce14cf6ae0","observation_id":"c5be5123-e1b6-4cb2-8ee1-fb71640ae462","resolution":{"observed_at":"2026-08-09T12:41:21.726962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-09T12:41:20.906946Z","title":"Scaling laws for neural language models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.906946Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:382549acf298511dba6bdd923e8a90cf7159c6a7fd802adfd4bbedb0f6e635ed","observation_id":"54785bc4-7b26-40bb-8477-ef2a48ca9d9d","resolution":{"observed_at":"2026-08-09T12:41:20.906946Z","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":"2026-08-09T12:41:21.715977Z","title":"Analyzing and improving the image quality of StyleGAN","venue":null,"work_id":"cb11b782-7ef8-4cb9-a577-957081c296af","year":2020},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.910846Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:0ee5c71dc59025c12f8bfb32f1ff9c29da8e8e0b76f4b06a666a9b4ccadfd6d7","observation_id":"8284d36f-e58d-4a55-a95a-ea60c0e5c1e2","resolution":{"observed_at":"2026-08-09T12:41:21.718804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.708139Z","title":"Using eye-tracking in education: review of empirical research and technology","venue":null,"work_id":"4cb27a4f-bd47-4704-ac35-f247073854c2","year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.913572Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:b48e15093465d1927afc8879a05299d8f6db2ef8b95509e830129968cd9c0a4d","observation_id":"b4d35eb5-919c-4231-8e2c-0c8a4d6dd45c","resolution":{"observed_at":"2026-08-09T12:41:21.711276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.700023Z","title":"Gaze360: Physically uncon- strained gaze estimation in the wild","venue":null,"work_id":"728b3a40-d9ed-487e-8937-ac3bba41d197","year":2019},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.916706Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:be479e7063d57e5d27fa478a60812dc2e8af2c6f8d38e7ffa5b9ce6909721361","observation_id":"84e81ff1-c8ae-48aa-a115-dd6b21fdf49f","resolution":{"observed_at":"2026-08-09T12:41:21.703394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.691932Z","title":"Sapiens: Foundation for human vision mod- els","venue":null,"work_id":"ae06fa56-6b8c-4b8e-a87b-aece13a37321","year":2025},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.919444Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:ea3462c62e21591dc8464f3eaeee57cc37a67eb50ee6a70b3831c606f038c391","observation_id":"152ddd95-2645-43f4-a1d5-273bd491c2a6","resolution":{"observed_at":"2026-08-09T12:41:21.694941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.683655Z","title":"Nvgaze: An anatomically-informed dataset for low-latency, near-eye gaze estimation","venue":null,"work_id":"20050de7-d83d-4888-a19d-c3bc1013b512","year":2019},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.922069Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:788d866c6f3c08c429b0d1741bcbcec3ec8dc3faef96bf830c88efa93e945652","observation_id":"8379e1f3-f504-442d-a533-1b6c8d8fb00e","resolution":{"observed_at":"2026-08-09T12:41:21.686749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.675832Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":"892bbfda-f745-4d34-8127-22f273a641f0","year":2015},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.925147Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:c95df329f6cbea9ee8de9556665d8d9502b41ef7610186795fc73d5496207630","observation_id":"a4b77863-4f4f-49a9-b858-f800c163322f","resolution":{"observed_at":"2026-08-09T12:41:21.678740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.667928Z","title":"Segment any- thing","venue":null,"work_id":"1b8a7f54-a5d8-403c-8385-2080c7d1b09a","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.928604Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:33fa5a4d76892d109aee71fb19fca672038369d2199b8392b8515444065a9d1d","observation_id":"6d4ec40f-77da-4fa8-ab99-dc78fbb53886","resolution":{"observed_at":"2026-08-09T12:41:21.670781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.659411Z","title":"Weakly-supervised physically unconstrained gaze estimation","venue":null,"work_id":"79786467-f76c-42ef-8c15-86abd97ed76e","year":2021},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.931276Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:aeb098ac662d21fb311fc250dba5db33b4cb66b3fef22c8d08eae90b5f702074","observation_id":"9d3a7b7d-216d-4af4-8968-dcec8094f9bd","resolution":{"observed_at":"2026-08-09T12:41:21.662730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.650674Z","title":"Eye tracking for everyone","venue":null,"work_id":"fe7ac27c-43c5-4108-8f4b-993b4c9fadd0","year":2016},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.933902Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:6bcc32be5ec75f1043c4159188b580c4c9449f49eaf8d36327205eee2e0382e8","observation_id":"90ab926b-72cc-4c2c-8893-de3e85c21677","resolution":{"observed_at":"2026-08-09T12:41:21.653671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.641510Z","title":"Masked autoen- coders for microscopy are scalable learners of cellular biol- ogy","venue":null,"work_id":"00a8e95d-13b7-4feb-90c4-45a1f225b157","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.937528Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:50986698e6bb75bc7faeb6d33d7f8c19323a967dbd1a448b97bf259609a6e5a6","observation_id":"41619302-8156-48f6-a5bd-08a77b3a9855","resolution":{"observed_at":"2026-08-09T12:41:21.645011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.633140Z","title":"Your diffusion model is se- cretly a zero-shot classifier","venue":null,"work_id":"3e77b2d0-083b-49b0-8e90-6cb694186e67","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.941290Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:ba471ecae1c978d7bc9c7588489e71794de96480ab3e3632586ef222ab367348","observation_id":"4e6c7c65-cbd3-4010-80f5-4905c5e56a55","resolution":{"observed_at":"2026-08-09T12:41:21.635863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.624938Z","title":"Dreamteacher: Pretraining image backbones with deep generative models","venue":null,"work_id":"ce66f66c-9086-489c-8128-98aae2ab0160","year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.945821Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:95e0ec5335c75c8663ef9f933beb8e9ba9d916a7e1b205e17e26c700fe165014","observation_id":"5fad05ab-93d1-4715-979d-10824e1dd314","resolution":{"observed_at":"2026-08-09T12:41:21.627923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.24223","last_updated":"2024-10-31T17:59:56Z","snapshot_observed_at":"2026-08-16T13:04:01.873819Z","submitted_at":"2024-10-31T17:59:56Z","title":"URAvatar: Universal Relightable Gaussian Codec Avatars","version":1},"cited_work":{"arxiv_id":"2410.24223","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.24223","snapshot_observed_at":"2026-08-09T12:41:21.245143Z","title":"URAvatar: Universal Relightable Gaussian Codec Avatars","venue":"cs.CV","work_id":"af6b4677-9a0f-45fb-b213-1491e1779e4d","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.949614Z"},"links":{"cited_paper":"/paper/2410.24223","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:e35fb6f571d320129eababb58931926e34fdfb0458b014c13b04b2d71c489535","observation_id":"dfb3b377-9b34-481c-b4b1-ae268ae7cd93","resolution":{"observed_at":"2026-08-09T12:41:21.248139Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10718","last_updated":"2025-02-05T10:39:02Z","snapshot_observed_at":"2026-08-16T14:00:24.412905Z","submitted_at":"2024-04-16T16:51:27Z","title":"GazeHTA: End-to-end Gaze Target Detection with Head-Target Association","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.10718","snapshot_observed_at":"2026-08-09T12:41:20.953422Z","title":"Gazehta: End-to-end gaze target detection with head- target association","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.953422Z"},"links":{"cited_paper":"/paper/2404.10718","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:eee3cdfbb3d6cec475d184ce1800d70e62e1f41a31ded37f0f20621f6fc79fa5","observation_id":"4ef56b30-f6f9-4414-8d8a-9c8b48e13023","resolution":{"observed_at":"2026-08-09T12:41:20.953422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02082","last_updated":"2022-10-05T08:20:41Z","snapshot_observed_at":"2026-08-16T16:26:48.537165Z","submitted_at":"2022-10-05T08:20:41Z","title":"Jitter Does Matter: Adapting Gaze Estimation to New Domains","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02082","snapshot_observed_at":"2026-08-09T12:41:20.957488Z","title":"Jitter does matter: Adapting gaze esti- mation to new domains","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.957488Z"},"links":{"cited_paper":"/paper/2210.02082","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:e9ee458045ed0b74859f10d07a6647b5241c490a4b76bf7d7d7b709aa0be9b3a","observation_id":"cd1cb8ad-b298-471b-8b0a-467be46ad8ee","resolution":{"observed_at":"2026-08-09T12:41:20.957488Z","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":"2026-08-09T12:41:21.617093Z","title":"Pnp- ga+: Plug-and-play domain adaptation for gaze estimation using model variants","venue":null,"work_id":"0b6edc7e-6f06-4ff9-b67b-cb5af7323b26","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.961812Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:cd8b4e59450406e4254892ea18ab17cf087bedda68b1f5ab16681d0b759b13be","observation_id":"b6418575-f62f-4815-8034-5e272c74f5de","resolution":{"observed_at":"2026-08-09T12:41:21.619623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.608176Z","title":"From gaze jitter to domain adaptation: Generalizing gaze estimation by ma- nipulating high-frequency components","venue":null,"work_id":"f5370850-1017-448b-96c2-596df1628df8","year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.965227Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:93ad785b53a8599ac7d95eabd2bce1b9fc0e741cc41eb12b30bc19bdbab52892","observation_id":"bd36b445-c4ab-467c-8328-7527e3cd6436","resolution":{"observed_at":"2026-08-09T12:41:21.611411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.599048Z","title":"Gen- eralizing gaze estimation with outlier-guided collaborative adaptation","venue":null,"work_id":"bf93ac63-7123-425b-ac52-6561ee631122","year":2021},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.968866Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:9c207b8844afb9f97b75771966d7523455254a82350412993a2209d4f7be6cd1","observation_id":"3287d030-aec4-43ca-9178-566b0596faf3","resolution":{"observed_at":"2026-08-09T12:41:21.602295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.590641Z","title":"Agisoft metashape","venue":null,"work_id":"ac116989-a3aa-48c5-958c-3d0e4a4c5025","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.972464Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:ebe2d01d8d406b15ed13f053360881aab28041be887e676a72e94fbb82eeb611","observation_id":"0ba10e0f-7c84-49e7-abe1-34ec9d02cc60","resolution":{"observed_at":"2026-08-09T12:41:21.593520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.582459Z","title":"Head pose-free appearance-based gaze sensing via eye im- age synthesis","venue":null,"work_id":"e462448c-e682-4739-81f0-1a11458462e6","year":2012},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.976014Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:c149f20de74facfab1b7ace7b8a80494f08a2956adb4d3dd098aa53cd4f12027","observation_id":"53e2bb1a-d89a-4594-9c87-1bccd1617a4c","resolution":{"observed_at":"2026-08-09T12:41:21.585511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.573905Z","title":"Learning gaze biases with head motion for head pose-free gaze estimation","venue":null,"work_id":"8b994393-6191-49c7-bd7a-c676ac11d477","year":2014},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.979738Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:54b0235bdef17909fd0f173db51df6b8326a65858d5569ea31602f69a7151f93","observation_id":"9c8bd719-0078-41e5-8b11-71d57dec378e","resolution":{"observed_at":"2026-08-09T12:41:21.577314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.565536Z","title":"Gaze-hand align- ment: Combining eye gaze and mid-air pointing for inter- acting with menus in augmented reality","venue":null,"work_id":"308f3517-ce8f-4fb7-b4bf-daa60ab80df6","year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.983200Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:f9a5d48d9a03250443adfba3e1b1328d0712c7cd759ebd97ae0ed36b81fec04c","observation_id":"c94fb147-91d6-4aec-a26f-1cb674544ed6","resolution":{"observed_at":"2026-08-09T12:41:21.568790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.557267Z","title":"Eye tracking and eye- based human–computer interaction","venue":null,"work_id":"4ca128a1-4797-414d-95e6-919fdb46ab31","year":2014},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.987112Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:22512776f3e3d40787c6701c58dd15d66feccb0fe1ea768e983655e0e27fbe5f","observation_id":"01ec922f-cb4c-429f-b1bb-9f3eaab55afa","resolution":{"observed_at":"2026-08-09T12:41:21.560359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.547943Z","title":"Few-shot adaptive gaze estimation","venue":null,"work_id":"8ac33576-49d7-4a0f-a864-7099c5f48cdf","year":2019},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.990667Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:f805b92aec77f742e2035cdd3a77a9f47bb2009818338bea44b50e7a5e8cf615","observation_id":"cd6b71ec-6864-49c8-b428-7fff1f670ab2","resolution":{"observed_at":"2026-08-09T12:41:21.551494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.538676Z","title":"Learning-by-novel-view-synthesis for full-face appearance- based 3d gaze estimation","venue":null,"work_id":"623ce062-dfc0-4ca7-b526-81fff8ef9ef9","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.994202Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:12705f1fd3de02dcb83c0531369fc22021f9ca28bd9dd00840376510da321180","observation_id":"f52aaf9f-3d00-4015-9976-16ed68ef74c9","resolution":{"observed_at":"2026-08-09T12:41:21.542074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16140","last_updated":"2024-07-07T19:06:10Z","snapshot_observed_at":"2026-08-16T15:29:40.685562Z","submitted_at":"2023-05-25T15:15:03Z","title":"Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement","version":2},"cited_work":{"arxiv_id":"2305.16140","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.16140","snapshot_observed_at":"2026-08-09T12:41:21.207237Z","title":"Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement","venue":"cs.CV","work_id":"0b412b1c-5e3a-4308-acc1-cdb52b408f8c","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:20.998042Z"},"links":{"cited_paper":"/paper/2305.16140","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:7dd580ff29ab9f52785a659f243b32edbc89aca9b950c9f54e2059b0e82c5c51","observation_id":"ebe8981a-b193-462e-b3b4-bf88727fc963","resolution":{"observed_at":"2026-08-09T12:41:21.211364Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.529778Z","title":"High-resolution image syn- thesis with latent diffusion models","venue":null,"work_id":"39ffe609-7f52-4a9d-8533-4e8634d99426","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.002206Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:f82464e80c798825c05ee1dcbd3028db3eddb0e99a5d082f56dcb74078d589cd","observation_id":"5f8e3563-f369-4b57-a799-c93ca32a237b","resolution":{"observed_at":"2026-08-09T12:41:21.532916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.521146Z","title":"Gazenerf: 3d-aware gaze redirection with neural radiance fields","venue":null,"work_id":"adc749e5-b02d-4ea6-90bc-3f01cc236b99","year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.005903Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:5fcdaee9332bb9e3371cf92382349ee1a8868a6c277d6a8d12945303d34695d4","observation_id":"3dd56781-df7a-46f6-aa24-587274d0e6b8","resolution":{"observed_at":"2026-08-09T12:41:21.524261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.512984Z","title":"A review of driver gaze estimation and application in gaze behavior understanding","venue":null,"work_id":"132cf0c5-79ff-4681-b5c2-4a82316f8253","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.009801Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:01b1113ab70c4acb099961867dc50e7d4ab12d76d8f29c01bd6690cdc758a647","observation_id":"def8033b-353e-4a0d-90da-1ed752fa3f58","resolution":{"observed_at":"2026-08-09T12:41:21.515915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.505609Z","title":"Agent-guided gaze estimation network by two-eye asymmetry exploration","venue":null,"work_id":"7c13f706-e8d1-41c2-b795-14d328090922","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.013559Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:a638e48b6c963a4211b2179055a4c3150724cd492d77cc4b1c298fe15acfe75c","observation_id":"879d82d3-61cd-45e1-ab0f-35952ee54ba5","resolution":{"observed_at":"2026-08-09T12:41:21.508237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.498750Z","title":"The effectiveness of mae pre-pretraining for billion- scale pretraining","venue":null,"work_id":"e035a874-37e6-4365-a88f-8256a7d7777c","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.017504Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:3a010d8ccc8f26096cc2848b035c9bbbf53a8f227e6e49e61ed7da1b2db48fa8","observation_id":"72bba800-a9a4-414e-baa5-e5e2832be733","resolution":{"observed_at":"2026-08-09T12:41:21.501177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.492069Z","title":"Super-convergence: Very fast training of neural networks using large learn- ing rates","venue":null,"work_id":"855b4838-37a3-4acb-acf4-3d2c40d7b875","year":2019},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.021155Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:9d97fc0e816584aa5ca55a50acdb3c7b8c29378289a3b35f5a034fcf398167d7","observation_id":"9e65d4f9-ed5f-4b49-84cf-b4c019663423","resolution":{"observed_at":"2026-08-09T12:41:21.494396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.484485Z","title":"Omnivec: Learn- ing robust representations with cross modal sharing","venue":null,"work_id":"34d91e66-3ade-4f41-9a78-0b88200f0094","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.024984Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:8f69cdc0cd712c83b2ec72601ee793188231ca5aa469a7cf042fe2822244dda0","observation_id":"7b24584e-e76f-4b70-b970-9ca88c13c357","resolution":{"observed_at":"2026-08-09T12:41:21.487443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20717","last_updated":"2024-10-28T04:19:32Z","snapshot_observed_at":"2026-08-17T13:00:32.428107Z","submitted_at":"2024-10-28T04:19:32Z","title":"Face-MLLM: A Large Face Perception Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20717","snapshot_observed_at":"2026-08-09T12:41:21.028540Z","title":"Face-mllm: A large face perception model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.028540Z"},"links":{"cited_paper":"/paper/2410.20717","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:974717548efece333acc562c55c032de01cdc1254f5bf2f5973451b805ed7c52","observation_id":"be86dae6-7a57-4fb1-b165-e991726d51f5","resolution":{"observed_at":"2026-08-09T12:41:21.028540Z","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":"2026-08-09T12:41:21.476536Z","title":"Appearance-based eye gaze estimation","venue":null,"work_id":"1258e69e-b331-4ad7-854a-b8cd822209eb","year":2002},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.032514Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:c5e4241c58a00a3a2b1764a9c36325ed234fcb632f75c4f87e9fefeda6799582","observation_id":"f4aeecd3-4a47-44f7-809a-bea5c91c88ca","resolution":{"observed_at":"2026-08-09T12:41:21.479589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.468424Z","title":"Videomae: Masked autoencoders are data-efficient learn- ers for self-supervised video pre-training","venue":null,"work_id":"a8a38e1d-5867-432e-8c2b-c0727d498da6","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.036133Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:14a8b42f6b32f122ce249173675b08f318f109042bad0dda615212b3e8fc9f21","observation_id":"8a1fbb65-eded-4c43-ba40-7b25b7b3e3d4","resolution":{"observed_at":"2026-08-09T12:41:21.471553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.460011Z","title":"3dgazenet: Generalizing 3d gaze estimation with weak- supervision from synthetic views","venue":null,"work_id":"53ca19c7-310b-4412-b79f-9de285c744d5","year":2025},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.039837Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:995c5a7e52658f07b390927f81050edd881420ac4495489af556603798256e48","observation_id":"6e4a70dd-06f8-4dc4-adb0-958c406411f0","resolution":{"observed_at":"2026-08-09T12:41:21.463121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.00773","last_updated":"2023-09-12T16:23:09Z","snapshot_observed_at":"2026-08-18T06:57:37.288801Z","submitted_at":"2023-08-01T18:26:55Z","title":"High-Fidelity Eye Animatable Neural Radiance Fields for Human Face","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.00773","snapshot_observed_at":"2026-08-09T12:41:21.042774Z","title":"High-fidelity eye animatable neu- ral radiance fields for human face","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.042774Z"},"links":{"cited_paper":"/paper/2308.00773","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:df2d88d422501873e260b2095f8f3bc2c69f91a54438e42f68ba1bbd8fd6e5bc","observation_id":"c61a5791-ac12-4f65-bb8b-b605e5d91431","resolution":{"observed_at":"2026-08-09T12:41:21.042774Z","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":"2026-08-09T12:41:21.452023Z","title":"Generaliz- ing eye tracking with bayesian adversarial learning","venue":null,"work_id":"d2f60fbb-c3d8-4f6d-9d61-1db67e5682ff","year":2019},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.046259Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:5f5e3f9c5a2a65147bf482dbb260abeb0f94592a833265e8ef2e1f4412191be9","observation_id":"9e9e8042-cfe3-488c-acd2-6e9fe07d8a5b","resolution":{"observed_at":"2026-08-09T12:41:21.455009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.443705Z","title":"Con- trastive regression for domain adaptation on gaze estimation","venue":null,"work_id":"15202cfe-d018-47f7-a267-46beb0a1864d","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.049432Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:35e3b749461daa5d8fb8370810f167a8ac43ef2b7b723a816444e0da546ba84e","observation_id":"0c7c6963-eab3-439d-b60c-8901709f773b","resolution":{"observed_at":"2026-08-09T12:41:21.447058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.435265Z","title":"Toward high qual- ity facial representation learning","venue":null,"work_id":"005282b1-1021-42d7-bb3b-a3137ce4ef8e","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.052286Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:8d1c735b70b59f3c3a4e3f05923c19ef4d80a3ccb043a21132e9151848c42c09","observation_id":"a533235d-cda4-4284-b796-f45a8ae03a27","resolution":{"observed_at":"2026-08-09T12:41:21.438361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.426734Z","title":"Rendering of eyes for eye-shape registration and gaze estimation","venue":null,"work_id":"10f122cc-3437-4352-bfed-1d7f38de02f4","year":2015},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.055236Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:8e91a538c79fb2cc82e14ffd7ebd97858b59fd8f9c3aaf21939d088ffa53653e","observation_id":"a866ee59-6c4c-4d49-a807-a3b8f2c4de37","resolution":{"observed_at":"2026-08-09T12:41:21.429928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.418593Z","title":"Fake it till you make it: face analysis in the wild using synthetic data alone","venue":null,"work_id":"3370cc9e-a798-42ad-9ac4-24e79a1af78c","year":2021},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.058067Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:79566940b909e3a16d5b280bc923aa29374a5ba355fc5bb9d308c54971dfcaa7","observation_id":"e179e58f-812c-4fe1-b906-5fd3cc5c925c","resolution":{"observed_at":"2026-08-09T12:41:21.421573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.409983Z","title":"Denoising diffusion autoencoders are unified self-supervised learners","venue":null,"work_id":"10180b0f-bfcf-43bd-b376-0e1938779735","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.060835Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:90916d5feb7f23ab3692a4f87dfe508eb225054abe2214bf915725991e2e0fc4","observation_id":"2f50e6a5-d354-4f19-a557-dcdb64282cca","resolution":{"observed_at":"2026-08-09T12:41:21.413002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.401823Z","title":"Vfhq: A high-quality dataset and benchmark for video face super-resolution","venue":null,"work_id":"6b3e4e4c-b0a8-407c-a01f-c776d9bd2019","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.063524Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:e26d7fdd99108d1bfad3a6e75981039691c3f2b14e989fe8bda6d394694af1a2","observation_id":"eb2e3b1a-5dc3-431c-8fc8-ca17eaa81160","resolution":{"observed_at":"2026-08-09T12:41:21.404971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.393904Z","title":"Gaze from origin: Learning for generalized gaze estimation by embedding the gaze frontal- ization process","venue":null,"work_id":"b8a619ac-dd6f-48d8-9e2d-796d2a4bc1c2","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.067115Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:2e37b918c53f73670f307973ae02d60ee220fca6616c5844d33c95595d3a7bd3","observation_id":"cb1b2662-19f4-4673-a6f9-b3e8fe626e78","resolution":{"observed_at":"2026-08-09T12:41:21.396516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.387173Z","title":"Learning a gener- alized gaze estimator from gaze-consistent feature","venue":null,"work_id":"ce2f7846-a6d4-4cf4-8f90-92d37b99d694","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.070865Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:48c68cb3eb9f8870b846015ff269f15fad72a1eb29962a5334a5822a238b993b","observation_id":"70c92584-4cad-45b3-9bd1-ba1a42d2462d","resolution":{"observed_at":"2026-08-09T12:41:21.389574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.14710","last_updated":"2022-12-30T13:52:28Z","snapshot_observed_at":"2026-08-16T16:05:15.661341Z","submitted_at":"2022-12-30T13:52:28Z","title":"NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation","version":1},"cited_work":{"arxiv_id":"2212.14710","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.14710","snapshot_observed_at":"2026-08-09T12:41:21.175399Z","title":"NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation","venue":"cs.CV","work_id":"aa8ec053-7f9f-4a22-8041-3ff606ea221b","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.074274Z"},"links":{"cited_paper":"/paper/2212.14710","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:07ece26ccd00c0cd37d68556499583b2608273bbe0d22f6847c4511c95b3d55f","observation_id":"bde7311b-0ead-43b0-a708-d67d4794485a","resolution":{"observed_at":"2026-08-09T12:41:21.179632Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.379951Z","title":"Lg-gaze: Learning geometry-aware continu- ous prompts for language-guided gaze estimation","venue":null,"work_id":"c06d68d1-f5cb-4c9d-a973-e8b5149cd804","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.078145Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:7ea5321bc8ee58c133be96d0ab8026fd2a9fff9d70dafb2b34d2c8aee257efd5","observation_id":"2144ee57-1e38-4c73-b56f-c54f4451a8ae","resolution":{"observed_at":"2026-08-09T12:41:21.382621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.372967Z","title":"Clip-gaze: Towards general gaze estimation via visual- linguistic model","venue":null,"work_id":"2cd700b6-03b5-4549-a67d-99e9a536e7de","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.081641Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:b0a027d8ec26a72f0fa1fb37737279f696664437e3b6924a95115061a05f5ab3","observation_id":"a20fa6d8-f502-424d-ada7-ff324884db20","resolution":{"observed_at":"2026-08-09T12:41:21.375514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.364759Z","title":"Celebv-text: A large-scale fa- cial text-video dataset","venue":null,"work_id":"366e4417-8e33-4c28-9b00-fdd8d20fb922","year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.085190Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:5a83187f3be5c526c5cb2ccde0980ef9b61a452b21a91f81c02daa9bc37cf9a0","observation_id":"6091caed-72a7-409c-b393-8bc0ec2e9b2e","resolution":{"observed_at":"2026-08-09T12:41:21.368011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.356158Z","title":"Appearance-based gaze estimation in the wild","venue":null,"work_id":"5ed918f1-ef20-4aa9-8481-84426da5f936","year":2015},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.088651Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:8553e5adf900762ca0e5c5b13cbcf52db8d9f81418f382272ac3f196007c09e9","observation_id":"6b030114-c7c8-41ed-b947-b3fbf3e113a7","resolution":{"observed_at":"2026-08-09T12:41:21.359249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.347565Z","title":"It’s written all over your face: Full-face appearance- based gaze estimation","venue":null,"work_id":"334644c1-cb45-4fce-bb56-8e877f7b4557","year":2017},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.091986Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:07b4cd40010317ba1c377a30cbd69835e8c75ad233c1b037e79986b6c541c661","observation_id":"bd728cc9-a45f-43c5-9dcb-77b7f9443344","resolution":{"observed_at":"2026-08-09T12:41:21.350800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.338622Z","title":"Re- visiting data normalization for appearance-based gaze esti- mation","venue":null,"work_id":"6644ed1b-7fe5-4143-afd8-9421b0d10530","year":2018},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.095573Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:90de6b4f73749e9d8cef351557a88ef4108374be8b89abd324c0810b0c669e9d","observation_id":"1f2b52cb-b4e8-4fda-bb7d-8ecd995c4d9b","resolution":{"observed_at":"2026-08-09T12:41:21.341908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.329299Z","title":"Mpiigaze: Real-world dataset and deep appearance- based gaze estimation","venue":null,"work_id":"1ed21ce6-fa33-4703-86b0-bbb589f5ccdc","year":2019},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.099346Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:09e9841f43c35b59bc70eac4f61924a41aa59b1ce57045d019f24dd8d57f7a48","observation_id":"9a659ea9-8930-45db-b78c-2726fb3708fd","resolution":{"observed_at":"2026-08-09T12:41:21.332666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.320054Z","title":"Eth-xgaze: A large scale dataset for gaze estimation under extreme head pose and gaze variation","venue":null,"work_id":"0cdadbf8-3f72-4c8f-8a48-459894d48ad0","year":null},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.102917Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:1a22c7ed4cfb3dafdda486fb92b0e514f9ee3496f75c5626abe34e8cea329888","observation_id":"f23cd7f5-67b1-4282-8f0e-3a1f12dece14","resolution":{"observed_at":"2026-08-09T12:41:21.323480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01439","last_updated":"2024-05-02T16:26:37Z","snapshot_observed_at":"2026-08-16T13:55:53.948261Z","submitted_at":"2024-05-02T16:26:37Z","title":"Improving Domain Generalization on Gaze Estimation via Branch-out Auxiliary Regularization","version":1},"cited_work":{"arxiv_id":"2405.01439","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.01439","snapshot_observed_at":"2026-08-09T12:41:21.156936Z","title":"Improving Domain Generalization on Gaze Estimation via Branch-out Auxiliary Regularization","venue":"cs.CV","work_id":"a47965b4-fa6f-43f1-a753-dab0c20d2602","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.106614Z"},"links":{"cited_paper":"/paper/2405.01439","citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:727d6c72a264e4306629abd18c543a1ad90ffa285135e310cea9036741b6bf80","observation_id":"d71249f8-abf1-44f3-83be-6aaca88762df","resolution":{"observed_at":"2026-08-09T12:41:21.162782Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.310269Z","title":"Unleashing text-to-image diffusion models for visual perception","venue":null,"work_id":"3b7e821a-c89a-4cac-bdc7-fa004d481a42","year":2023},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.110545Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:7e7971cd17bafa489613f27539d4f6822f42c46fd8125742351ca780969879ad","observation_id":"f572dd30-e592-45dd-8c0a-0f58300a4bea","resolution":{"observed_at":"2026-08-09T12:41:21.313817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.301525Z","title":"Self-learning transformations for improving gaze and head redirection","venue":null,"work_id":"53bca10a-bfd4-448a-bb9b-5aaa28832b2c","year":2020},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.114296Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:6aa89469ad4733245233eb76cbb8d16312691cd70062e08e3757fceb16163dcb","observation_id":"907820ce-daa8-4bb7-a762-416188fadff9","resolution":{"observed_at":"2026-08-09T12:41:21.304570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.292859Z","title":"General facial representation learning in a visual-linguistic manner","venue":null,"work_id":"30333a8c-f867-4760-8280-06f2fdd443f5","year":2022},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.117905Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:2f5f9e124167404b45c75d333995f9f89682f71126619d286a489459b1f0f467","observation_id":"e921fd75-9073-4b8f-8568-7f87847152c6","resolution":{"observed_at":"2026-08-09T12:41:21.296139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.283835Z","title":"Places: A 10 million image database for scene recognition","venue":null,"work_id":"6381219d-cf88-48f2-b362-b59bc5201d9c","year":2017},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.121395Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:b5daa2a4fd55b08624dea0b8355ad43c9fc5004fe5327ff0ebc72364a2e79b82","observation_id":"5bedd294-3929-46f4-bd5c-f4b0e3e1a0c4","resolution":{"observed_at":"2026-08-09T12:41:21.286977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-09T12:41:21.275074Z","title":"Deformable one- shot face stylization via dino semantic guidance","venue":null,"work_id":"85d00d55-d3a5-46f4-8b34-e915fd1eff8b","year":2024},"citing_paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-09T12:41:21.124880Z"},"links":{"citing_paper":"/paper/2502.02307"},"observation_digest":"sha256:a3dd693f5ccb0e06f9203999ecf25490deef6c98f60fb3e02b75c5c6497dc4bd","observation_id":"0ea4b30d-89d2-484a-a0f9-a099eaa3774b","resolution":{"observed_at":"2026-08-09T12:41:21.278249Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.02307","last_updated":"2025-03-13T15:59:03Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T18:18:39.931662Z","submitted_at":"2025-02-04T13:24:23Z","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training"},"reference_resolution":{"displayed":98,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":25,"verified_exact":5,"verified_fuzzy":67},"total_outbound_references":98},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 98 of 98 outbound references and 0 inbound Pith citation observations for arXiv:2502.02307."}