{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7WMSIFSTEB7ZZMLMARSGCRRVO2","short_pith_number":"pith:7WMSIFST","schema_version":"1.0","canonical_sha256":"fd99241653207f9cb16c046461463576b745d9fc36ef7161338ff5f2586bc1d7","source":{"kind":"arxiv","id":"2412.02935","version":2},"attestation_state":"computed","paper":{"title":"Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Keqin Li, Tao Meng, Wei Ai, Yuntao Shou","submitted_at":"2024-12-04T01:07:59Z","abstract_excerpt":"Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying human emotional states by combining data from multiple different modalities (e.g., audio, images, text, video, etc.). Most existing multimodal emotion recognition methods use GCN to improve performance, but existing GCN methods are prone to overfitting and cannot capture the temporal dependency of the speaker's emotions. To address the above problems, we propose a Dynamic Graph Neural Ordinary Differential Equation Network (DGODE) for MERC, which combines the dynamic changes of emotions to capture the 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":"2412.02935","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-04T01:07:59Z","cross_cats_sorted":[],"title_canon_sha256":"985f23093d84809da0ce50aa1bf598424ddc4bbcfbb6f2b4a283a950afec00ab","abstract_canon_sha256":"81c3f48c1a5e0d188cfb1de4e52a7cb4adc58664576d8155c377d7b97f0f1eba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:16.404603Z","signature_b64":"mgmHBvoPiqm2T4eM38k9nZ9Kd+Iqlc2o6nslwKul/as2YDSMxWehQw8/IJUUzeh6tgRvVIvEHONURKofohb+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd99241653207f9cb16c046461463576b745d9fc36ef7161338ff5f2586bc1d7","last_reissued_at":"2026-07-05T11:52:16.403773Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:16.403773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Keqin Li, Tao Meng, Wei Ai, Yuntao Shou","submitted_at":"2024-12-04T01:07:59Z","abstract_excerpt":"Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying human emotional states by combining data from multiple different modalities (e.g., audio, images, text, video, etc.). Most existing multimodal emotion recognition methods use GCN to improve performance, but existing GCN methods are prone to overfitting and cannot capture the temporal dependency of the speaker's emotions. To address the above problems, we propose a Dynamic Graph Neural Ordinary Differential Equation Network (DGODE) for MERC, which combines the dynamic changes of emotions to capture the t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.02935","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/2412.02935/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":"2412.02935","created_at":"2026-07-05T11:52:16.403858+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.02935v2","created_at":"2026-07-05T11:52:16.403858+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.02935","created_at":"2026-07-05T11:52:16.403858+00:00"},{"alias_kind":"pith_short_12","alias_value":"7WMSIFSTEB7Z","created_at":"2026-07-05T11:52:16.403858+00:00"},{"alias_kind":"pith_short_16","alias_value":"7WMSIFSTEB7ZZMLM","created_at":"2026-07-05T11:52:16.403858+00:00"},{"alias_kind":"pith_short_8","alias_value":"7WMSIFST","created_at":"2026-07-05T11:52:16.403858+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.11450","citing_title":"GroupFace: Imbalanced Age Estimation Based on Multi-hop Attention Graph Convolutional Network and Group-aware Margin Optimization","ref_index":46,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2","json":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2.json","graph_json":"https://pith.science/api/pith-number/7WMSIFSTEB7ZZMLMARSGCRRVO2/graph.json","events_json":"https://pith.science/api/pith-number/7WMSIFSTEB7ZZMLMARSGCRRVO2/events.json","paper":"https://pith.science/paper/7WMSIFST"},"agent_actions":{"view_html":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2","download_json":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2.json","view_paper":"https://pith.science/paper/7WMSIFST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.02935&json=true","fetch_graph":"https://pith.science/api/pith-number/7WMSIFSTEB7ZZMLMARSGCRRVO2/graph.json","fetch_events":"https://pith.science/api/pith-number/7WMSIFSTEB7ZZMLMARSGCRRVO2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2/action/storage_attestation","attest_author":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2/action/author_attestation","sign_citation":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2/action/citation_signature","submit_replication":"https://pith.science/pith/7WMSIFSTEB7ZZMLMARSGCRRVO2/action/replication_record"}},"created_at":"2026-07-05T11:52:16.403858+00:00","updated_at":"2026-07-05T11:52:16.403858+00:00"}