{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:R2I6OPJ6MYJFXBOIU2UBGHHBQV","short_pith_number":"pith:R2I6OPJ6","schema_version":"1.0","canonical_sha256":"8e91e73d3e66125b85c8a6a8131ce1857e83b4cc751883dd1cca6f0120151d80","source":{"kind":"arxiv","id":"2407.09029","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Emotion Recognition in Incomplete Data: A Novel Cross-Modal Alignment, Reconstruction, and Refinement Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.SD","eess.AS"],"primary_cat":"cs.MM","authors_text":"Aobo Kong, Haoqin Sun, Jiaming Zhou, Shaokai Li, Shiwan Zhao, Wenjia Zeng, Xiangyu Kong, Xuechen Wang, Yong Chen, Yong Qin","submitted_at":"2024-07-12T06:44:42Z","abstract_excerpt":"Multimodal emotion recognition systems rely heavily on the full availability of modalities, suffering significant performance declines when modal data is incomplete. To tackle this issue, we present the Cross-Modal Alignment, Reconstruction, and Refinement (CM-ARR) framework, an innovative approach that sequentially engages in cross-modal alignment, reconstruction, and refinement phases to handle missing modalities and enhance emotion recognition. This framework utilizes unsupervised distribution-based contrastive learning to align heterogeneous modal distributions, reducing discrepancies and "},"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":"2407.09029","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2024-07-12T06:44:42Z","cross_cats_sorted":["cs.CV","cs.SD","eess.AS"],"title_canon_sha256":"f1b28fa3ccad6f6e7712dfd5800693373b040852d30ea6a2f982a36593a8b7cf","abstract_canon_sha256":"a8ef356812b9d808ff1622e1ca797a0a06458b295fad40c1053a5aa5554edd41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:13.468510Z","signature_b64":"Wy3I5Dxn2Udi3bosiyftPAdy4uoPoLdTnqUGmOqZIiIkVb6CedbCYB0ElaWnGYAJlTnLiI8jWIWsEazVgewXCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e91e73d3e66125b85c8a6a8131ce1857e83b4cc751883dd1cca6f0120151d80","last_reissued_at":"2026-07-05T08:43:13.468069Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:13.468069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Emotion Recognition in Incomplete Data: A Novel Cross-Modal Alignment, Reconstruction, and Refinement Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.SD","eess.AS"],"primary_cat":"cs.MM","authors_text":"Aobo Kong, Haoqin Sun, Jiaming Zhou, Shaokai Li, Shiwan Zhao, Wenjia Zeng, Xiangyu Kong, Xuechen Wang, Yong Chen, Yong Qin","submitted_at":"2024-07-12T06:44:42Z","abstract_excerpt":"Multimodal emotion recognition systems rely heavily on the full availability of modalities, suffering significant performance declines when modal data is incomplete. To tackle this issue, we present the Cross-Modal Alignment, Reconstruction, and Refinement (CM-ARR) framework, an innovative approach that sequentially engages in cross-modal alignment, reconstruction, and refinement phases to handle missing modalities and enhance emotion recognition. This framework utilizes unsupervised distribution-based contrastive learning to align heterogeneous modal distributions, reducing discrepancies and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.09029","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/2407.09029/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":"2407.09029","created_at":"2026-07-05T08:43:13.468134+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.09029v1","created_at":"2026-07-05T08:43:13.468134+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.09029","created_at":"2026-07-05T08:43:13.468134+00:00"},{"alias_kind":"pith_short_12","alias_value":"R2I6OPJ6MYJF","created_at":"2026-07-05T08:43:13.468134+00:00"},{"alias_kind":"pith_short_16","alias_value":"R2I6OPJ6MYJFXBOI","created_at":"2026-07-05T08:43:13.468134+00:00"},{"alias_kind":"pith_short_8","alias_value":"R2I6OPJ6","created_at":"2026-07-05T08:43:13.468134+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/R2I6OPJ6MYJFXBOIU2UBGHHBQV","json":"https://pith.science/pith/R2I6OPJ6MYJFXBOIU2UBGHHBQV.json","graph_json":"https://pith.science/api/pith-number/R2I6OPJ6MYJFXBOIU2UBGHHBQV/graph.json","events_json":"https://pith.science/api/pith-number/R2I6OPJ6MYJFXBOIU2UBGHHBQV/events.json","paper":"https://pith.science/paper/R2I6OPJ6"},"agent_actions":{"view_html":"https://pith.science/pith/R2I6OPJ6MYJFXBOIU2UBGHHBQV","download_json":"https://pith.science/pith/R2I6OPJ6MYJFXBOIU2UBGHHBQV.json","view_paper":"https://pith.science/paper/R2I6OPJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.09029&json=true","fetch_graph":"https://pith.science/api/pith-number/R2I6OPJ6MYJFXBOIU2UBGHHBQV/graph.json","fetch_events":"https://pith.science/api/pith-number/R2I6OPJ6MYJFXBOIU2UBGHHBQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R2I6OPJ6MYJFXBOIU2UBGHHBQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R2I6OPJ6MYJFXBOIU2UBGHHBQV/action/storage_attestation","attest_author":"https://pith.science/pith/R2I6OPJ6MYJFXBOIU2UBGHHBQV/action/author_attestation","sign_citation":"https://pith.science/pith/R2I6OPJ6MYJFXBOIU2UBGHHBQV/action/citation_signature","submit_replication":"https://pith.science/pith/R2I6OPJ6MYJFXBOIU2UBGHHBQV/action/replication_record"}},"created_at":"2026-07-05T08:43:13.468134+00:00","updated_at":"2026-07-05T08:43:13.468134+00:00"}