{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7LDH54RLLGXETPZAYETKYI32LU","short_pith_number":"pith:7LDH54RL","schema_version":"1.0","canonical_sha256":"fac67ef22b59ae49bf20c126ac237a5d2d5d2e45ec9cc959ad20fd76527de775","source":{"kind":"arxiv","id":"2306.02301","version":1},"attestation_state":"computed","paper":{"title":"rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Lu, Huanjing Yue, Jingyu Yang, Xin Liu, Yuting Zhang, Zitong Yu","submitted_at":"2023-06-04T08:53:28Z","abstract_excerpt":"Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior "},"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":"2306.02301","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-04T08:53:28Z","cross_cats_sorted":[],"title_canon_sha256":"bfc1c50fefe2a64b0ffa48385579bdde9fe2459d4eb88c0e91aec2bb7069895c","abstract_canon_sha256":"11dd17c016b82f0ba643f818cb280024c4321ed996021c75623c979be6e5de79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:17:15.488122Z","signature_b64":"WxLxnyy63zBDjCOMpOPCOQkgCWeTiriezYUiVZzDI8Y/EpOrxRMi0JcgoWU2FsuFn0c4JlC2bMpKiF/qGbyeCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fac67ef22b59ae49bf20c126ac237a5d2d5d2e45ec9cc959ad20fd76527de775","last_reissued_at":"2026-07-05T06:17:15.487702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:17:15.487702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Lu, Huanjing Yue, Jingyu Yang, Xin Liu, Yuting Zhang, Zitong Yu","submitted_at":"2023-06-04T08:53:28Z","abstract_excerpt":"Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.02301","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/2306.02301/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":"2306.02301","created_at":"2026-07-05T06:17:15.487758+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.02301v1","created_at":"2026-07-05T06:17:15.487758+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.02301","created_at":"2026-07-05T06:17:15.487758+00:00"},{"alias_kind":"pith_short_12","alias_value":"7LDH54RLLGXE","created_at":"2026-07-05T06:17:15.487758+00:00"},{"alias_kind":"pith_short_16","alias_value":"7LDH54RLLGXETPZA","created_at":"2026-07-05T06:17:15.487758+00:00"},{"alias_kind":"pith_short_8","alias_value":"7LDH54RL","created_at":"2026-07-05T06:17:15.487758+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.07526","citing_title":"CodePhys: Robust Video-based Remote Physiological Measurement through Latent Codebook Querying","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU","json":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU.json","graph_json":"https://pith.science/api/pith-number/7LDH54RLLGXETPZAYETKYI32LU/graph.json","events_json":"https://pith.science/api/pith-number/7LDH54RLLGXETPZAYETKYI32LU/events.json","paper":"https://pith.science/paper/7LDH54RL"},"agent_actions":{"view_html":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU","download_json":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU.json","view_paper":"https://pith.science/paper/7LDH54RL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.02301&json=true","fetch_graph":"https://pith.science/api/pith-number/7LDH54RLLGXETPZAYETKYI32LU/graph.json","fetch_events":"https://pith.science/api/pith-number/7LDH54RLLGXETPZAYETKYI32LU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU/action/storage_attestation","attest_author":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU/action/author_attestation","sign_citation":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU/action/citation_signature","submit_replication":"https://pith.science/pith/7LDH54RLLGXETPZAYETKYI32LU/action/replication_record"}},"created_at":"2026-07-05T06:17:15.487758+00:00","updated_at":"2026-07-05T06:17:15.487758+00:00"}