{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NTSKTVNU5GTIV22QZNYW7OVTKT","short_pith_number":"pith:NTSKTVNU","schema_version":"1.0","canonical_sha256":"6ce4a9d5b4e9a68aeb50cb716fbab354e1cd0778ffb094751595f84fa30a156b","source":{"kind":"arxiv","id":"2506.14181","version":1},"attestation_state":"computed","paper":{"title":"Meta-SurDiff: Classification Diffusion Model Optimized by Meta Learning is Reliable for Online Surgical Phase Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jirui Wu, Liming Wang, Long Tian, Xiaonan Liu, Xiyang Liu, Yufei Li, Zijun Liu","submitted_at":"2025-06-17T04:48:59Z","abstract_excerpt":"Online surgical phase recognition has drawn great attention most recently due to its potential downstream applications closely related to human life and health. Despite deep models have made significant advances in capturing the discriminative long-term dependency of surgical videos to achieve improved recognition, they rarely account for exploring and modeling the uncertainty in surgical videos, which should be crucial for reliable online surgical phase recognition. We categorize the sources of uncertainty into two types, frame ambiguity in videos and unbalanced distribution among surgical ph"},"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":"2506.14181","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-17T04:48:59Z","cross_cats_sorted":[],"title_canon_sha256":"84ad4ab490da762f8aac06905b7f47b38c4fcb1002c1a9da8ee6aba2c768b99a","abstract_canon_sha256":"ddf817a3c5669aedb51fb736c493b611601789ac8fc42e26666d282337891939"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:50.448772Z","signature_b64":"vjVD7h8oe5x0hFPrX/GSNZe+DuY0Jdh56WLvTUheAjGc4aGbghctluTWnM9lezI4WfL16vFqNkMmIH9UimqbBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ce4a9d5b4e9a68aeb50cb716fbab354e1cd0778ffb094751595f84fa30a156b","last_reissued_at":"2026-07-05T11:22:50.448279Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:50.448279Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta-SurDiff: Classification Diffusion Model Optimized by Meta Learning is Reliable for Online Surgical Phase Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jirui Wu, Liming Wang, Long Tian, Xiaonan Liu, Xiyang Liu, Yufei Li, Zijun Liu","submitted_at":"2025-06-17T04:48:59Z","abstract_excerpt":"Online surgical phase recognition has drawn great attention most recently due to its potential downstream applications closely related to human life and health. Despite deep models have made significant advances in capturing the discriminative long-term dependency of surgical videos to achieve improved recognition, they rarely account for exploring and modeling the uncertainty in surgical videos, which should be crucial for reliable online surgical phase recognition. We categorize the sources of uncertainty into two types, frame ambiguity in videos and unbalanced distribution among surgical ph"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14181","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/2506.14181/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":"2506.14181","created_at":"2026-07-05T11:22:50.448342+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.14181v1","created_at":"2026-07-05T11:22:50.448342+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14181","created_at":"2026-07-05T11:22:50.448342+00:00"},{"alias_kind":"pith_short_12","alias_value":"NTSKTVNU5GTI","created_at":"2026-07-05T11:22:50.448342+00:00"},{"alias_kind":"pith_short_16","alias_value":"NTSKTVNU5GTIV22Q","created_at":"2026-07-05T11:22:50.448342+00:00"},{"alias_kind":"pith_short_8","alias_value":"NTSKTVNU","created_at":"2026-07-05T11:22:50.448342+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/NTSKTVNU5GTIV22QZNYW7OVTKT","json":"https://pith.science/pith/NTSKTVNU5GTIV22QZNYW7OVTKT.json","graph_json":"https://pith.science/api/pith-number/NTSKTVNU5GTIV22QZNYW7OVTKT/graph.json","events_json":"https://pith.science/api/pith-number/NTSKTVNU5GTIV22QZNYW7OVTKT/events.json","paper":"https://pith.science/paper/NTSKTVNU"},"agent_actions":{"view_html":"https://pith.science/pith/NTSKTVNU5GTIV22QZNYW7OVTKT","download_json":"https://pith.science/pith/NTSKTVNU5GTIV22QZNYW7OVTKT.json","view_paper":"https://pith.science/paper/NTSKTVNU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.14181&json=true","fetch_graph":"https://pith.science/api/pith-number/NTSKTVNU5GTIV22QZNYW7OVTKT/graph.json","fetch_events":"https://pith.science/api/pith-number/NTSKTVNU5GTIV22QZNYW7OVTKT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NTSKTVNU5GTIV22QZNYW7OVTKT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NTSKTVNU5GTIV22QZNYW7OVTKT/action/storage_attestation","attest_author":"https://pith.science/pith/NTSKTVNU5GTIV22QZNYW7OVTKT/action/author_attestation","sign_citation":"https://pith.science/pith/NTSKTVNU5GTIV22QZNYW7OVTKT/action/citation_signature","submit_replication":"https://pith.science/pith/NTSKTVNU5GTIV22QZNYW7OVTKT/action/replication_record"}},"created_at":"2026-07-05T11:22:50.448342+00:00","updated_at":"2026-07-05T11:22:50.448342+00:00"}