{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:22D4CSUAGXN4TTMN7DCMWQ2PEN","short_pith_number":"pith:22D4CSUA","schema_version":"1.0","canonical_sha256":"d687c14a8035dbc9cd8df8c4cb434f234198f16bf3b457f7f2d5a1fff9584384","source":{"kind":"arxiv","id":"2507.15765","version":2},"attestation_state":"computed","paper":{"title":"Learning from Heterogeneity: Generalizing Dynamic Facial Expression Recognition via Distributionally Robust Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anyang Tong, Dan Guo, Feng-Qi Cui, Jie Zhang, Jinyang Huang, Meng Wang, Zhi Liu","submitted_at":"2025-07-21T16:21:47Z","abstract_excerpt":"Dynamic Facial Expression Recognition (DFER) plays a critical role in affective computing and human-computer interaction. Although existing methods achieve comparable performance, they inevitably suffer from performance degradation under sample heterogeneity caused by multi-source data and individual expression variability. To address these challenges, we propose a novel framework, called Heterogeneity-aware Distributional Framework (HDF), and design two plug-and-play modules to enhance time-frequency modeling and mitigate optimization imbalance caused by hard samples. Specifically, the Time-F"},"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":"2507.15765","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-21T16:21:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"166db4d2255c715f1d4c2a137eb8a7814d265a8ac18120f19a62bcc7d580dca0","abstract_canon_sha256":"dc6ae584d3deb45249276e373d9e25e2ad8d207aaa7d334c664b30d32624d66f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:33.976037Z","signature_b64":"t7tgxh+arnrKJzVIYk3wrYVfeDaxrZYyTsIU8ii64QRXZZkXOK0E3GaPZUazTybeJG5Fikwor9b2kQUWRWIUCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d687c14a8035dbc9cd8df8c4cb434f234198f16bf3b457f7f2d5a1fff9584384","last_reissued_at":"2026-07-05T11:43:33.975569Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:33.975569Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning from Heterogeneity: Generalizing Dynamic Facial Expression Recognition via Distributionally Robust Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anyang Tong, Dan Guo, Feng-Qi Cui, Jie Zhang, Jinyang Huang, Meng Wang, Zhi Liu","submitted_at":"2025-07-21T16:21:47Z","abstract_excerpt":"Dynamic Facial Expression Recognition (DFER) plays a critical role in affective computing and human-computer interaction. Although existing methods achieve comparable performance, they inevitably suffer from performance degradation under sample heterogeneity caused by multi-source data and individual expression variability. To address these challenges, we propose a novel framework, called Heterogeneity-aware Distributional Framework (HDF), and design two plug-and-play modules to enhance time-frequency modeling and mitigate optimization imbalance caused by hard samples. Specifically, the Time-F"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.15765","kind":"arxiv","version":2},"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/2507.15765/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":"2507.15765","created_at":"2026-07-05T11:43:33.975632+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.15765v2","created_at":"2026-07-05T11:43:33.975632+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.15765","created_at":"2026-07-05T11:43:33.975632+00:00"},{"alias_kind":"pith_short_12","alias_value":"22D4CSUAGXN4","created_at":"2026-07-05T11:43:33.975632+00:00"},{"alias_kind":"pith_short_16","alias_value":"22D4CSUAGXN4TTMN","created_at":"2026-07-05T11:43:33.975632+00:00"},{"alias_kind":"pith_short_8","alias_value":"22D4CSUA","created_at":"2026-07-05T11:43:33.975632+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01424","citing_title":"Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN","json":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN.json","graph_json":"https://pith.science/api/pith-number/22D4CSUAGXN4TTMN7DCMWQ2PEN/graph.json","events_json":"https://pith.science/api/pith-number/22D4CSUAGXN4TTMN7DCMWQ2PEN/events.json","paper":"https://pith.science/paper/22D4CSUA"},"agent_actions":{"view_html":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN","download_json":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN.json","view_paper":"https://pith.science/paper/22D4CSUA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.15765&json=true","fetch_graph":"https://pith.science/api/pith-number/22D4CSUAGXN4TTMN7DCMWQ2PEN/graph.json","fetch_events":"https://pith.science/api/pith-number/22D4CSUAGXN4TTMN7DCMWQ2PEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN/action/storage_attestation","attest_author":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN/action/author_attestation","sign_citation":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN/action/citation_signature","submit_replication":"https://pith.science/pith/22D4CSUAGXN4TTMN7DCMWQ2PEN/action/replication_record"}},"created_at":"2026-07-05T11:43:33.975632+00:00","updated_at":"2026-07-05T11:43:33.975632+00:00"}