{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Y3GZKXY7IJ7JNAPATBS54MECQY","short_pith_number":"pith:Y3GZKXY7","schema_version":"1.0","canonical_sha256":"c6cd955f1f427e9681e09865de308286304c3d85610df617ec2665a8257a98fa","source":{"kind":"arxiv","id":"2412.14489","version":3},"attestation_state":"computed","paper":{"title":"Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"C.L.Philip Chen, Shu Shen, Tong Zhang","submitted_at":"2024-12-19T03:26:51Z","abstract_excerpt":"Multimodal machine learning has achieved remarkable progress in many scenarios, but its reliability is undermined by varying sample quality. This paper finds that existing reliable multimodal classification methods not only fail to provide robust estimation of data quality, but also lack dynamic networks for sample-specific depth and parameters to achieve reliable inference. To this end, a novel framework for multimodal reliable classification termed \\textit{Multi-level Quality-Adaptive Dynamic multimodal network} (Multi-QuAD) is proposed. Multi-QuAD first adopts a novel approach based on nois"},"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":"2412.14489","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-19T03:26:51Z","cross_cats_sorted":[],"title_canon_sha256":"9d271f6e10ab72b36e1edcd3decd172fe9c50d29229adbe4aeaa4d854aae6f97","abstract_canon_sha256":"8c9452dd7bbaf1d9bb8d62d9f1b3a23507ef512de45efae9a38f1b32b4b46fec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:02.749980Z","signature_b64":"I7PI1dpx+mWeasi4J3T0eBrKeeEWNNoudg0M60OqhTc30vUiukBPJeK9BtoAor5DwPI19oE0Ll5pKooq8EDIBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6cd955f1f427e9681e09865de308286304c3d85610df617ec2665a8257a98fa","last_reissued_at":"2026-07-05T11:01:02.749404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:02.749404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"C.L.Philip Chen, Shu Shen, Tong Zhang","submitted_at":"2024-12-19T03:26:51Z","abstract_excerpt":"Multimodal machine learning has achieved remarkable progress in many scenarios, but its reliability is undermined by varying sample quality. This paper finds that existing reliable multimodal classification methods not only fail to provide robust estimation of data quality, but also lack dynamic networks for sample-specific depth and parameters to achieve reliable inference. To this end, a novel framework for multimodal reliable classification termed \\textit{Multi-level Quality-Adaptive Dynamic multimodal network} (Multi-QuAD) is proposed. Multi-QuAD first adopts a novel approach based on nois"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14489","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/2412.14489/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":"2412.14489","created_at":"2026-07-05T11:01:02.749466+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.14489v3","created_at":"2026-07-05T11:01:02.749466+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14489","created_at":"2026-07-05T11:01:02.749466+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y3GZKXY7IJ7J","created_at":"2026-07-05T11:01:02.749466+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y3GZKXY7IJ7JNAPA","created_at":"2026-07-05T11:01:02.749466+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y3GZKXY7","created_at":"2026-07-05T11:01:02.749466+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/Y3GZKXY7IJ7JNAPATBS54MECQY","json":"https://pith.science/pith/Y3GZKXY7IJ7JNAPATBS54MECQY.json","graph_json":"https://pith.science/api/pith-number/Y3GZKXY7IJ7JNAPATBS54MECQY/graph.json","events_json":"https://pith.science/api/pith-number/Y3GZKXY7IJ7JNAPATBS54MECQY/events.json","paper":"https://pith.science/paper/Y3GZKXY7"},"agent_actions":{"view_html":"https://pith.science/pith/Y3GZKXY7IJ7JNAPATBS54MECQY","download_json":"https://pith.science/pith/Y3GZKXY7IJ7JNAPATBS54MECQY.json","view_paper":"https://pith.science/paper/Y3GZKXY7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.14489&json=true","fetch_graph":"https://pith.science/api/pith-number/Y3GZKXY7IJ7JNAPATBS54MECQY/graph.json","fetch_events":"https://pith.science/api/pith-number/Y3GZKXY7IJ7JNAPATBS54MECQY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y3GZKXY7IJ7JNAPATBS54MECQY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y3GZKXY7IJ7JNAPATBS54MECQY/action/storage_attestation","attest_author":"https://pith.science/pith/Y3GZKXY7IJ7JNAPATBS54MECQY/action/author_attestation","sign_citation":"https://pith.science/pith/Y3GZKXY7IJ7JNAPATBS54MECQY/action/citation_signature","submit_replication":"https://pith.science/pith/Y3GZKXY7IJ7JNAPATBS54MECQY/action/replication_record"}},"created_at":"2026-07-05T11:01:02.749466+00:00","updated_at":"2026-07-05T11:01:02.749466+00:00"}