{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SKNJVUJP3BYRH23YMFDCWQXCW4","short_pith_number":"pith:SKNJVUJP","schema_version":"1.0","canonical_sha256":"929a9ad12fd87113eb7861462b42e2b707d7f18fefcf63a6c930651d28345622","source":{"kind":"arxiv","id":"2507.06821","version":3},"attestation_state":"computed","paper":{"title":"HeLo: Heterogeneous Multi-Modal Fusion with Label Correlation for Emotion Distribution Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.LG","authors_text":"Chuhang Zheng, Chunwei Tian, Daoqiang Zhang, Jie Wen, Qi Zhu","submitted_at":"2025-07-09T13:08:58Z","abstract_excerpt":"Multi-modal emotion recognition has garnered increasing attention as it plays a significant role in human-computer interaction (HCI) in recent years. Since different discrete emotions may exist at the same time, compared with single-class emotion recognition, emotion distribution learning (EDL) that identifies a mixture of basic emotions has gradually emerged as a trend. However, existing EDL methods face challenges in mining the heterogeneity among multiple modalities. Besides, rich semantic correlations across arbitrary basic emotions are not fully exploited. In this paper, we propose a mult"},"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":"2507.06821","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-09T13:08:58Z","cross_cats_sorted":["cs.AI","cs.MM"],"title_canon_sha256":"a1577e94e1d22c5c19e66a642a7b43ae49d3edb3e442c5071051eaa7894c1138","abstract_canon_sha256":"046f436818ec63fdaec565985dab5da9dec26d011367c5a245a0f47c7f2a0bdd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:33.403875Z","signature_b64":"kXkwbcz2bUV7TkPyaNHsr88a2SfZD9ubRX9ijOy1x0kn15UC4uKjaQyJkgmi2NjJLh05qofiEKQ8MNFpNi7MAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"929a9ad12fd87113eb7861462b42e2b707d7f18fefcf63a6c930651d28345622","last_reissued_at":"2026-07-05T11:43:33.403418Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:33.403418Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HeLo: Heterogeneous Multi-Modal Fusion with Label Correlation for Emotion Distribution Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.LG","authors_text":"Chuhang Zheng, Chunwei Tian, Daoqiang Zhang, Jie Wen, Qi Zhu","submitted_at":"2025-07-09T13:08:58Z","abstract_excerpt":"Multi-modal emotion recognition has garnered increasing attention as it plays a significant role in human-computer interaction (HCI) in recent years. Since different discrete emotions may exist at the same time, compared with single-class emotion recognition, emotion distribution learning (EDL) that identifies a mixture of basic emotions has gradually emerged as a trend. However, existing EDL methods face challenges in mining the heterogeneity among multiple modalities. Besides, rich semantic correlations across arbitrary basic emotions are not fully exploited. In this paper, we propose a mult"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06821","kind":"arxiv","version":3},"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/2507.06821/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":"2507.06821","created_at":"2026-07-05T11:43:33.403487+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.06821v3","created_at":"2026-07-05T11:43:33.403487+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06821","created_at":"2026-07-05T11:43:33.403487+00:00"},{"alias_kind":"pith_short_12","alias_value":"SKNJVUJP3BYR","created_at":"2026-07-05T11:43:33.403487+00:00"},{"alias_kind":"pith_short_16","alias_value":"SKNJVUJP3BYRH23Y","created_at":"2026-07-05T11:43:33.403487+00:00"},{"alias_kind":"pith_short_8","alias_value":"SKNJVUJP","created_at":"2026-07-05T11:43:33.403487+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/SKNJVUJP3BYRH23YMFDCWQXCW4","json":"https://pith.science/pith/SKNJVUJP3BYRH23YMFDCWQXCW4.json","graph_json":"https://pith.science/api/pith-number/SKNJVUJP3BYRH23YMFDCWQXCW4/graph.json","events_json":"https://pith.science/api/pith-number/SKNJVUJP3BYRH23YMFDCWQXCW4/events.json","paper":"https://pith.science/paper/SKNJVUJP"},"agent_actions":{"view_html":"https://pith.science/pith/SKNJVUJP3BYRH23YMFDCWQXCW4","download_json":"https://pith.science/pith/SKNJVUJP3BYRH23YMFDCWQXCW4.json","view_paper":"https://pith.science/paper/SKNJVUJP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.06821&json=true","fetch_graph":"https://pith.science/api/pith-number/SKNJVUJP3BYRH23YMFDCWQXCW4/graph.json","fetch_events":"https://pith.science/api/pith-number/SKNJVUJP3BYRH23YMFDCWQXCW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SKNJVUJP3BYRH23YMFDCWQXCW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SKNJVUJP3BYRH23YMFDCWQXCW4/action/storage_attestation","attest_author":"https://pith.science/pith/SKNJVUJP3BYRH23YMFDCWQXCW4/action/author_attestation","sign_citation":"https://pith.science/pith/SKNJVUJP3BYRH23YMFDCWQXCW4/action/citation_signature","submit_replication":"https://pith.science/pith/SKNJVUJP3BYRH23YMFDCWQXCW4/action/replication_record"}},"created_at":"2026-07-05T11:43:33.403487+00:00","updated_at":"2026-07-05T11:43:33.403487+00:00"}