{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:T7NHTEP34XVII7WPY5AXOAKJG6","short_pith_number":"pith:T7NHTEP3","schema_version":"1.0","canonical_sha256":"9fda7991fbe5ea847ecfc7417701493797d598f54f9197df4bc5d7e18cc58972","source":{"kind":"arxiv","id":"2403.10825","version":1},"attestation_state":"computed","paper":{"title":"Affective Behaviour Analysis via Integrating Multi-Modal Knowledge","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Liu, Feng Qiu, Heming Du, Lincheng Li, Tiancheng Guo, Wei Zhang, Xin Yu","submitted_at":"2024-03-16T06:26:43Z","abstract_excerpt":"Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do. To comprehensively evaluate the authenticity and applicability of emotional behavior analysis techniques in natural environments, the 6th competition on Affective Behavior Analysis in-the-wild (ABAW) utilizes the Aff-Wild2, Hume-Vidmimic2, and C-EXPR-DB datasets to set up five competitive tracks, i.e., Valence-Arousal (VA) Estimation, Expression (EXPR) Recognition, Action Unit (AU) Detection, Compound Expression (CE) Recognition, and"},"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":"2403.10825","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-16T06:26:43Z","cross_cats_sorted":[],"title_canon_sha256":"57f734262afe9b1a5ff9684c99a3a638331d5eb482263813f4b28968a268ce52","abstract_canon_sha256":"e338d977a3e532694b059db0436b35d8b8c94c571c7dd63df8f390e12271e7d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:07.611402Z","signature_b64":"80sDFVhDN6iHjfUxM33Cw9ylsBZ1gnc44XzoOt+iJxvArOme2vRLBRXTbyxGYT+8wPg/9usz6dbcN/CpEZUyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fda7991fbe5ea847ecfc7417701493797d598f54f9197df4bc5d7e18cc58972","last_reissued_at":"2026-07-05T07:57:07.610956Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:07.610956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Affective Behaviour Analysis via Integrating Multi-Modal Knowledge","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Liu, Feng Qiu, Heming Du, Lincheng Li, Tiancheng Guo, Wei Zhang, Xin Yu","submitted_at":"2024-03-16T06:26:43Z","abstract_excerpt":"Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do. To comprehensively evaluate the authenticity and applicability of emotional behavior analysis techniques in natural environments, the 6th competition on Affective Behavior Analysis in-the-wild (ABAW) utilizes the Aff-Wild2, Hume-Vidmimic2, and C-EXPR-DB datasets to set up five competitive tracks, i.e., Valence-Arousal (VA) Estimation, Expression (EXPR) Recognition, Action Unit (AU) Detection, Compound Expression (CE) Recognition, and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.10825","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/2403.10825/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":"2403.10825","created_at":"2026-07-05T07:57:07.611010+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.10825v1","created_at":"2026-07-05T07:57:07.611010+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.10825","created_at":"2026-07-05T07:57:07.611010+00:00"},{"alias_kind":"pith_short_12","alias_value":"T7NHTEP34XVI","created_at":"2026-07-05T07:57:07.611010+00:00"},{"alias_kind":"pith_short_16","alias_value":"T7NHTEP34XVII7WP","created_at":"2026-07-05T07:57:07.611010+00:00"},{"alias_kind":"pith_short_8","alias_value":"T7NHTEP3","created_at":"2026-07-05T07:57:07.611010+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25634","citing_title":"SSMNBench: Diagnosing Image-based Cross-View Human-Object Understanding via Single-View Sufficiency and Multi-View Necessity","ref_index":80,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21869","citing_title":"Two-Stage Multimodal Framework for Emotion Mimicry Intensity Prediction","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6","json":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6.json","graph_json":"https://pith.science/api/pith-number/T7NHTEP34XVII7WPY5AXOAKJG6/graph.json","events_json":"https://pith.science/api/pith-number/T7NHTEP34XVII7WPY5AXOAKJG6/events.json","paper":"https://pith.science/paper/T7NHTEP3"},"agent_actions":{"view_html":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6","download_json":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6.json","view_paper":"https://pith.science/paper/T7NHTEP3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.10825&json=true","fetch_graph":"https://pith.science/api/pith-number/T7NHTEP34XVII7WPY5AXOAKJG6/graph.json","fetch_events":"https://pith.science/api/pith-number/T7NHTEP34XVII7WPY5AXOAKJG6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6/action/storage_attestation","attest_author":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6/action/author_attestation","sign_citation":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6/action/citation_signature","submit_replication":"https://pith.science/pith/T7NHTEP34XVII7WPY5AXOAKJG6/action/replication_record"}},"created_at":"2026-07-05T07:57:07.611010+00:00","updated_at":"2026-07-05T07:57:07.611010+00:00"}