{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2CAWSPQ5CQJXXZWR6AUNQC3EWH","short_pith_number":"pith:2CAWSPQ5","schema_version":"1.0","canonical_sha256":"d081693e1d14137be6d1f028d80b64b1c8a6bba8864e3ffe2e7c05bbff9c51fc","source":{"kind":"arxiv","id":"2502.20128","version":3},"attestation_state":"computed","paper":{"title":"Differential Contrastive Training for Gaze Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lin Zhang, Wanru Xu, XiYun Wang, Yaping Huang, Yi Jin, Yi Tian","submitted_at":"2025-02-27T14:23:20Z","abstract_excerpt":"The complex application scenarios have raised critical requirements for precise and generalizable gaze estimation methods. Recently, the pre-trained CLIP has achieved remarkable performance on various vision tasks, but its potentials have not been fully exploited in gaze estimation. In this paper, we propose a novel Differential Contrastive Training strategy, which boosts gaze estimation performance with the help of the CLIP. Accordingly, a Differential Contrastive Gaze Estimation network (DCGaze) composed of a Visual Appearance-aware branch and a Semantic Differential-aware branch is introduc"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.20128","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-27T14:23:20Z","cross_cats_sorted":[],"title_canon_sha256":"37a578efd0354a3bfb4d9b2ccbb7f6b0826b346df40a1aeb6375f4a844b7c165","abstract_canon_sha256":"583f3aef56ca4fc413af857cb525acc26277d9be8c03b571ff274825061d66c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:31.944476Z","signature_b64":"/hhxaJGTelZDtKO8lr36Ujngfm9uicrHqQY6WpUtmxhI5eewbACD5RIa2U7+YOmjZRL4X75uCyqSd+8hEhkuBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d081693e1d14137be6d1f028d80b64b1c8a6bba8864e3ffe2e7c05bbff9c51fc","last_reissued_at":"2026-07-05T11:45:31.943973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:31.943973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differential Contrastive Training for Gaze Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lin Zhang, Wanru Xu, XiYun Wang, Yaping Huang, Yi Jin, Yi Tian","submitted_at":"2025-02-27T14:23:20Z","abstract_excerpt":"The complex application scenarios have raised critical requirements for precise and generalizable gaze estimation methods. Recently, the pre-trained CLIP has achieved remarkable performance on various vision tasks, but its potentials have not been fully exploited in gaze estimation. In this paper, we propose a novel Differential Contrastive Training strategy, which boosts gaze estimation performance with the help of the CLIP. Accordingly, a Differential Contrastive Gaze Estimation network (DCGaze) composed of a Visual Appearance-aware branch and a Semantic Differential-aware branch is introduc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.20128","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.20128/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.20128","created_at":"2026-07-05T11:45:31.944035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.20128v3","created_at":"2026-07-05T11:45:31.944035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.20128","created_at":"2026-07-05T11:45:31.944035+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CAWSPQ5CQJX","created_at":"2026-07-05T11:45:31.944035+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CAWSPQ5CQJXXZWR","created_at":"2026-07-05T11:45:31.944035+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CAWSPQ5","created_at":"2026-07-05T11:45:31.944035+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH","json":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH.json","graph_json":"https://pith.science/api/pith-number/2CAWSPQ5CQJXXZWR6AUNQC3EWH/graph.json","events_json":"https://pith.science/api/pith-number/2CAWSPQ5CQJXXZWR6AUNQC3EWH/events.json","paper":"https://pith.science/paper/2CAWSPQ5"},"agent_actions":{"view_html":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH","download_json":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH.json","view_paper":"https://pith.science/paper/2CAWSPQ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.20128&json=true","fetch_graph":"https://pith.science/api/pith-number/2CAWSPQ5CQJXXZWR6AUNQC3EWH/graph.json","fetch_events":"https://pith.science/api/pith-number/2CAWSPQ5CQJXXZWR6AUNQC3EWH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH/action/storage_attestation","attest_author":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH/action/author_attestation","sign_citation":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH/action/citation_signature","submit_replication":"https://pith.science/pith/2CAWSPQ5CQJXXZWR6AUNQC3EWH/action/replication_record"}},"created_at":"2026-07-05T11:45:31.944035+00:00","updated_at":"2026-07-05T11:45:31.944035+00:00"}