{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EGJCV77L3BWTG3AW5JEJL2ZPWG","short_pith_number":"pith:EGJCV77L","schema_version":"1.0","canonical_sha256":"21922affebd86d336c16ea4895eb2fb1acfd854f04666bbbe94e735c3d8bb22f","source":{"kind":"arxiv","id":"2409.05015","version":2},"attestation_state":"computed","paper":{"title":"Improving Multimodal Emotion Recognition by Leveraging Acoustic Adaptation and Visual Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.HC","authors_text":"Dongmei Jiang, Haifeng Chen, Lei Xie, Xi Li, Zhixian Zhao","submitted_at":"2024-09-08T07:56:51Z","abstract_excerpt":"Multimodal Emotion Recognition (MER) aims to automatically identify and understand human emotional states by integrating information from various modalities. However, the scarcity of annotated multimodal data significantly hinders the advancement of this research field. This paper presents our solution for the MER-SEMI sub-challenge of MER 2024. First, to better adapt acoustic modality features for the MER task, we experimentally evaluate the contributions of different layers of the pre-trained speech model HuBERT in emotion recognition. Based on these observations, we perform Parameter-Effici"},"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":"2409.05015","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-09-08T07:56:51Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"cce28df53db4dd91489b59db4e6fbb9b1233fc2f70798e8385b1c6422233f503","abstract_canon_sha256":"50ec45dec24387cfcd58169cdc481f1f0297902085496be3db7e73952e7a190a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:05:19.549812Z","signature_b64":"tMjtLQlVkpNiEBQZQzWTRa7r8lJLFyQHWewC7/Wvfq2iIyLO8PcWQBVY3kLx54EFNSn64sQoiWcCcUlD+1UmCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21922affebd86d336c16ea4895eb2fb1acfd854f04666bbbe94e735c3d8bb22f","last_reissued_at":"2026-07-05T09:05:19.549374Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:05:19.549374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Multimodal Emotion Recognition by Leveraging Acoustic Adaptation and Visual Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.HC","authors_text":"Dongmei Jiang, Haifeng Chen, Lei Xie, Xi Li, Zhixian Zhao","submitted_at":"2024-09-08T07:56:51Z","abstract_excerpt":"Multimodal Emotion Recognition (MER) aims to automatically identify and understand human emotional states by integrating information from various modalities. However, the scarcity of annotated multimodal data significantly hinders the advancement of this research field. This paper presents our solution for the MER-SEMI sub-challenge of MER 2024. First, to better adapt acoustic modality features for the MER task, we experimentally evaluate the contributions of different layers of the pre-trained speech model HuBERT in emotion recognition. Based on these observations, we perform Parameter-Effici"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.05015","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/2409.05015/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":"2409.05015","created_at":"2026-07-05T09:05:19.549433+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.05015v2","created_at":"2026-07-05T09:05:19.549433+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.05015","created_at":"2026-07-05T09:05:19.549433+00:00"},{"alias_kind":"pith_short_12","alias_value":"EGJCV77L3BWT","created_at":"2026-07-05T09:05:19.549433+00:00"},{"alias_kind":"pith_short_16","alias_value":"EGJCV77L3BWTG3AW","created_at":"2026-07-05T09:05:19.549433+00:00"},{"alias_kind":"pith_short_8","alias_value":"EGJCV77L","created_at":"2026-07-05T09:05:19.549433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25325","citing_title":"Omni-Perception Policy Optimization for Multimodal Emotion Reasoning","ref_index":103,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG","json":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG.json","graph_json":"https://pith.science/api/pith-number/EGJCV77L3BWTG3AW5JEJL2ZPWG/graph.json","events_json":"https://pith.science/api/pith-number/EGJCV77L3BWTG3AW5JEJL2ZPWG/events.json","paper":"https://pith.science/paper/EGJCV77L"},"agent_actions":{"view_html":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG","download_json":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG.json","view_paper":"https://pith.science/paper/EGJCV77L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.05015&json=true","fetch_graph":"https://pith.science/api/pith-number/EGJCV77L3BWTG3AW5JEJL2ZPWG/graph.json","fetch_events":"https://pith.science/api/pith-number/EGJCV77L3BWTG3AW5JEJL2ZPWG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG/action/storage_attestation","attest_author":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG/action/author_attestation","sign_citation":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG/action/citation_signature","submit_replication":"https://pith.science/pith/EGJCV77L3BWTG3AW5JEJL2ZPWG/action/replication_record"}},"created_at":"2026-07-05T09:05:19.549433+00:00","updated_at":"2026-07-05T09:05:19.549433+00:00"}