{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:J6CALDTYK6ATEIU7PBABWP3HBQ","short_pith_number":"pith:J6CALDTY","schema_version":"1.0","canonical_sha256":"4f84058e78578132229f78401b3f670c26f57e509d697d5d0ac5cc70ed45dc9c","source":{"kind":"arxiv","id":"2207.14513","version":3},"attestation_state":"computed","paper":{"title":"Uncertainty-Driven Action Quality Assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Caixia Zhou, Yaping Huang","submitted_at":"2022-07-29T07:21:15Z","abstract_excerpt":"Automatic action quality assessment (AQA) has attracted increasing attention due to its wide applications. However, most existing AQA methods employ deterministic models to predict the final score for each action, while overlooking the subjectivity and diversity among expert judges during the scoring process. In this paper, we propose a novel probabilistic model, named Uncertainty-Driven AQA (UD-AQA), to utilize and capture the diversity among multiple judge scores. Specifically, we design a Conditional Variational Auto-Encoder (CVAE)-based module to encode the uncertainty in expert assessment"},"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":"2207.14513","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-29T07:21:15Z","cross_cats_sorted":[],"title_canon_sha256":"b68396a0bfc522eefe980f122ea42b3dd239472e5b86e3b0ba7e280dcb992cbf","abstract_canon_sha256":"31f2eb97864ebf28873bb623acf07202a0ceb1d30f24b245abe1c6c54667111e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:19.889051Z","signature_b64":"DYMfSdBcyyz7o0hZtYCCpianXaCGxIyojynDzbdLyMICBDV8RsqvHxp+ZeVGaiqkq75oLc2sFMK8+Epfg57FCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f84058e78578132229f78401b3f670c26f57e509d697d5d0ac5cc70ed45dc9c","last_reissued_at":"2026-07-05T09:56:19.888625Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:19.888625Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Uncertainty-Driven Action Quality Assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Caixia Zhou, Yaping Huang","submitted_at":"2022-07-29T07:21:15Z","abstract_excerpt":"Automatic action quality assessment (AQA) has attracted increasing attention due to its wide applications. However, most existing AQA methods employ deterministic models to predict the final score for each action, while overlooking the subjectivity and diversity among expert judges during the scoring process. In this paper, we propose a novel probabilistic model, named Uncertainty-Driven AQA (UD-AQA), to utilize and capture the diversity among multiple judge scores. Specifically, we design a Conditional Variational Auto-Encoder (CVAE)-based module to encode the uncertainty in expert assessment"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.14513","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/2207.14513/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":"2207.14513","created_at":"2026-07-05T09:56:19.888681+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.14513v3","created_at":"2026-07-05T09:56:19.888681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.14513","created_at":"2026-07-05T09:56:19.888681+00:00"},{"alias_kind":"pith_short_12","alias_value":"J6CALDTYK6AT","created_at":"2026-07-05T09:56:19.888681+00:00"},{"alias_kind":"pith_short_16","alias_value":"J6CALDTYK6ATEIU7","created_at":"2026-07-05T09:56:19.888681+00:00"},{"alias_kind":"pith_short_8","alias_value":"J6CALDTY","created_at":"2026-07-05T09:56:19.888681+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07438","citing_title":"Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment","ref_index":99,"is_internal_anchor":true},{"citing_arxiv_id":"2412.11149","citing_title":"A Comprehensive Survey of Action Quality Assessment: Method and Benchmark","ref_index":62,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ","json":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ.json","graph_json":"https://pith.science/api/pith-number/J6CALDTYK6ATEIU7PBABWP3HBQ/graph.json","events_json":"https://pith.science/api/pith-number/J6CALDTYK6ATEIU7PBABWP3HBQ/events.json","paper":"https://pith.science/paper/J6CALDTY"},"agent_actions":{"view_html":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ","download_json":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ.json","view_paper":"https://pith.science/paper/J6CALDTY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.14513&json=true","fetch_graph":"https://pith.science/api/pith-number/J6CALDTYK6ATEIU7PBABWP3HBQ/graph.json","fetch_events":"https://pith.science/api/pith-number/J6CALDTYK6ATEIU7PBABWP3HBQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ/action/storage_attestation","attest_author":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ/action/author_attestation","sign_citation":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ/action/citation_signature","submit_replication":"https://pith.science/pith/J6CALDTYK6ATEIU7PBABWP3HBQ/action/replication_record"}},"created_at":"2026-07-05T09:56:19.888681+00:00","updated_at":"2026-07-05T09:56:19.888681+00:00"}