{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KZYMSE54CAFBX3BVMZHA66EXPP","short_pith_number":"pith:KZYMSE54","schema_version":"1.0","canonical_sha256":"5670c913bc100a1bec35664e0f78977bec7992514c33e4c6830dd61471ca32bb","source":{"kind":"arxiv","id":"2410.17622","version":1},"attestation_state":"computed","paper":{"title":"Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bairong Shen, Jie Song, Jinhua Feng, Mengqiao He","submitted_at":"2024-10-23T07:26:19Z","abstract_excerpt":"In recent years, Facial Expression Recognition (FER) has gained increasing attention. Most current work focuses on supervised learning, which requires a large amount of labeled and diverse images, while FER suffers from the scarcity of large, diverse datasets and annotation difficulty. To address these problems, we focus on utilizing large unlabeled Face Recognition (FR) datasets to boost semi-supervised FER. Specifically, we first perform face reconstruction pre-training on large-scale facial images without annotations to learn features of facial geometry and expression regions, followed by t"},"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":"2410.17622","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-23T07:26:19Z","cross_cats_sorted":[],"title_canon_sha256":"e9b5738ec703f5c8e11d564a33b64a36b0516a492bcfe6f04aa514228379d17e","abstract_canon_sha256":"3fc5babe011074a4f08d5105b509d3781403a3755e1abb1a083b7fc486212bed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:24:41.680351Z","signature_b64":"LuHlqTwZd6mH5AZL3gXpv1dA6nYkhBjvQcFHPFAzmWrARbN1Y0gZzpiD87l7kXAkLOMQNAa8Blacq8TQuvBLBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5670c913bc100a1bec35664e0f78977bec7992514c33e4c6830dd61471ca32bb","last_reissued_at":"2026-07-05T09:24:41.679791Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:24:41.679791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bairong Shen, Jie Song, Jinhua Feng, Mengqiao He","submitted_at":"2024-10-23T07:26:19Z","abstract_excerpt":"In recent years, Facial Expression Recognition (FER) has gained increasing attention. Most current work focuses on supervised learning, which requires a large amount of labeled and diverse images, while FER suffers from the scarcity of large, diverse datasets and annotation difficulty. To address these problems, we focus on utilizing large unlabeled Face Recognition (FR) datasets to boost semi-supervised FER. Specifically, we first perform face reconstruction pre-training on large-scale facial images without annotations to learn features of facial geometry and expression regions, followed by t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.17622","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/2410.17622/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":"2410.17622","created_at":"2026-07-05T09:24:41.679877+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.17622v1","created_at":"2026-07-05T09:24:41.679877+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.17622","created_at":"2026-07-05T09:24:41.679877+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZYMSE54CAFB","created_at":"2026-07-05T09:24:41.679877+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZYMSE54CAFBX3BV","created_at":"2026-07-05T09:24:41.679877+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZYMSE54","created_at":"2026-07-05T09:24:41.679877+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/KZYMSE54CAFBX3BVMZHA66EXPP","json":"https://pith.science/pith/KZYMSE54CAFBX3BVMZHA66EXPP.json","graph_json":"https://pith.science/api/pith-number/KZYMSE54CAFBX3BVMZHA66EXPP/graph.json","events_json":"https://pith.science/api/pith-number/KZYMSE54CAFBX3BVMZHA66EXPP/events.json","paper":"https://pith.science/paper/KZYMSE54"},"agent_actions":{"view_html":"https://pith.science/pith/KZYMSE54CAFBX3BVMZHA66EXPP","download_json":"https://pith.science/pith/KZYMSE54CAFBX3BVMZHA66EXPP.json","view_paper":"https://pith.science/paper/KZYMSE54","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.17622&json=true","fetch_graph":"https://pith.science/api/pith-number/KZYMSE54CAFBX3BVMZHA66EXPP/graph.json","fetch_events":"https://pith.science/api/pith-number/KZYMSE54CAFBX3BVMZHA66EXPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZYMSE54CAFBX3BVMZHA66EXPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZYMSE54CAFBX3BVMZHA66EXPP/action/storage_attestation","attest_author":"https://pith.science/pith/KZYMSE54CAFBX3BVMZHA66EXPP/action/author_attestation","sign_citation":"https://pith.science/pith/KZYMSE54CAFBX3BVMZHA66EXPP/action/citation_signature","submit_replication":"https://pith.science/pith/KZYMSE54CAFBX3BVMZHA66EXPP/action/replication_record"}},"created_at":"2026-07-05T09:24:41.679877+00:00","updated_at":"2026-07-05T09:24:41.679877+00:00"}