{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KWQABU2T6YKHXTA5XUQDS5RXG2","short_pith_number":"pith:KWQABU2T","schema_version":"1.0","canonical_sha256":"55a000d353f6147bcc1dbd20397637368050f578a7a34443b96bd7ccaee249b3","source":{"kind":"arxiv","id":"2409.12040","version":1},"attestation_state":"computed","paper":{"title":"SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingjie Wu, Linlin Shen, Weicheng Xie, Yiping Xie, Zitong Yu","submitted_at":"2024-09-18T14:59:30Z","abstract_excerpt":"Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue i"},"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":"2409.12040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-18T14:59:30Z","cross_cats_sorted":[],"title_canon_sha256":"22df1665328eb4fdce2fa7331cf44644ccb369976cada983a6fa6d8757cc233b","abstract_canon_sha256":"46b8cf16289dda763220d65c30169bcf9114888f893a62ffdc3328db3a8c3dd0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:08:41.612430Z","signature_b64":"u/Rbqj+BkwtdUawcrViRQlnsGqL9IuCJn1ikBixlrD3OUDrWRxW/SXrOQkWwepyLtbheyGmWQE0V7Cnp30JvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55a000d353f6147bcc1dbd20397637368050f578a7a34443b96bd7ccaee249b3","last_reissued_at":"2026-07-05T09:08:41.611938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:08:41.611938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingjie Wu, Linlin Shen, Weicheng Xie, Yiping Xie, Zitong Yu","submitted_at":"2024-09-18T14:59:30Z","abstract_excerpt":"Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.12040","kind":"arxiv","version":1},"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/2409.12040/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":"2409.12040","created_at":"2026-07-05T09:08:41.612000+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.12040v1","created_at":"2026-07-05T09:08:41.612000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.12040","created_at":"2026-07-05T09:08:41.612000+00:00"},{"alias_kind":"pith_short_12","alias_value":"KWQABU2T6YKH","created_at":"2026-07-05T09:08:41.612000+00:00"},{"alias_kind":"pith_short_16","alias_value":"KWQABU2T6YKHXTA5","created_at":"2026-07-05T09:08:41.612000+00:00"},{"alias_kind":"pith_short_8","alias_value":"KWQABU2T","created_at":"2026-07-05T09:08:41.612000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.00882","citing_title":"Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography","ref_index":59,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2","json":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2.json","graph_json":"https://pith.science/api/pith-number/KWQABU2T6YKHXTA5XUQDS5RXG2/graph.json","events_json":"https://pith.science/api/pith-number/KWQABU2T6YKHXTA5XUQDS5RXG2/events.json","paper":"https://pith.science/paper/KWQABU2T"},"agent_actions":{"view_html":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2","download_json":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2.json","view_paper":"https://pith.science/paper/KWQABU2T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.12040&json=true","fetch_graph":"https://pith.science/api/pith-number/KWQABU2T6YKHXTA5XUQDS5RXG2/graph.json","fetch_events":"https://pith.science/api/pith-number/KWQABU2T6YKHXTA5XUQDS5RXG2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2/action/storage_attestation","attest_author":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2/action/author_attestation","sign_citation":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2/action/citation_signature","submit_replication":"https://pith.science/pith/KWQABU2T6YKHXTA5XUQDS5RXG2/action/replication_record"}},"created_at":"2026-07-05T09:08:41.612000+00:00","updated_at":"2026-07-05T09:08:41.612000+00:00"}