{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RNHVV45XC7ED3SELEIPZF4ITNF","short_pith_number":"pith:RNHVV45X","schema_version":"1.0","canonical_sha256":"8b4f5af3b717c83dc88b221f92f113697aee8b975efcd5879f382dd1df0edb5c","source":{"kind":"arxiv","id":"2103.15792","version":1},"attestation_state":"computed","paper":{"title":"Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Dimitrios Kollias, Stefanos Zafeiriou","submitted_at":"2021-03-29T17:36:20Z","abstract_excerpt":"Affect recognition based on subjects' facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This paper exploits these advances and presents significant contributions for affect analysis"},"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":"2103.15792","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-29T17:36:20Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"71f624b221aea6cd76e0aec26a04f7ea466b401cd6c7b5a537e39a1daafd7cf2","abstract_canon_sha256":"24317b7f1e4da85c35f827483ed5bef2277231bd1113d5fa3d62a379a71fc17e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:27:09.765238Z","signature_b64":"3OVPx/IOdG7dXYcqHRHaroQJObAq2A187rSx6lR7gWIo3lDP2CLsC/9cGgLY+135ixXf7yf1di07YCyreNrqCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b4f5af3b717c83dc88b221f92f113697aee8b975efcd5879f382dd1df0edb5c","last_reissued_at":"2026-07-05T02:27:09.764781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:27:09.764781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Dimitrios Kollias, Stefanos Zafeiriou","submitted_at":"2021-03-29T17:36:20Z","abstract_excerpt":"Affect recognition based on subjects' facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This paper exploits these advances and presents significant contributions for affect analysis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.15792","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/2103.15792/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":"2103.15792","created_at":"2026-07-05T02:27:09.764831+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.15792v1","created_at":"2026-07-05T02:27:09.764831+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.15792","created_at":"2026-07-05T02:27:09.764831+00:00"},{"alias_kind":"pith_short_12","alias_value":"RNHVV45XC7ED","created_at":"2026-07-05T02:27:09.764831+00:00"},{"alias_kind":"pith_short_16","alias_value":"RNHVV45XC7ED3SEL","created_at":"2026-07-05T02:27:09.764831+00:00"},{"alias_kind":"pith_short_8","alias_value":"RNHVV45X","created_at":"2026-07-05T02:27:09.764831+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.12221","citing_title":"A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10106","citing_title":"VGGT-HPE: Reframing Head Pose Estimation as Relative Pose Prediction","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07198","citing_title":"Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07193","citing_title":"LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF","json":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF.json","graph_json":"https://pith.science/api/pith-number/RNHVV45XC7ED3SELEIPZF4ITNF/graph.json","events_json":"https://pith.science/api/pith-number/RNHVV45XC7ED3SELEIPZF4ITNF/events.json","paper":"https://pith.science/paper/RNHVV45X"},"agent_actions":{"view_html":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF","download_json":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF.json","view_paper":"https://pith.science/paper/RNHVV45X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.15792&json=true","fetch_graph":"https://pith.science/api/pith-number/RNHVV45XC7ED3SELEIPZF4ITNF/graph.json","fetch_events":"https://pith.science/api/pith-number/RNHVV45XC7ED3SELEIPZF4ITNF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF/action/storage_attestation","attest_author":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF/action/author_attestation","sign_citation":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF/action/citation_signature","submit_replication":"https://pith.science/pith/RNHVV45XC7ED3SELEIPZF4ITNF/action/replication_record"}},"created_at":"2026-07-05T02:27:09.764831+00:00","updated_at":"2026-07-05T02:27:09.764831+00:00"}