{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QJ7ZRNEXGLPIISYAUFMNRFIBYU","short_pith_number":"pith:QJ7ZRNEX","schema_version":"1.0","canonical_sha256":"827f98b49732de844b00a158d89501c504249213dfdff91eb28241805d6e08eb","source":{"kind":"arxiv","id":"2306.08859","version":1},"attestation_state":"computed","paper":{"title":"SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Amer Ghanem, Bharti Goel, Bokai Zhang, Mohammad Hasan Sarhan, Svetlana Petculescu","submitted_at":"2023-06-15T05:04:29Z","abstract_excerpt":"Automatic surgical phase recognition is one of the key technologies to support Video-Based Assessment (VBA) systems for surgical education. Utilizing temporal information is crucial for surgical phase recognition, hence various recent approaches extract frame-level features to conduct full video temporal modeling. For better temporal modeling, we propose SlowFast Temporal Modeling Network (SF-TMN) for surgical phase recognition that can not only achieve frame-level full video temporal modeling but also achieve segment-level full video temporal modeling. We employ a feature extraction network, "},"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":"2306.08859","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-15T05:04:29Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"2bfc63c4a97bc58628a20b6096db95d1d2f45cab7eddde7c71a3a8f429ec354b","abstract_canon_sha256":"95389f2e3b3e085a345943df085e4dd4f5e75a94ab0b9d1cb244e346555d4437"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:44.507175Z","signature_b64":"CLWPWq3TvsH+VvDCZTns433J5fAj5l0AVmxf8hPtuxc3eVkWuHzsKbxIbYKQnskvRkFKphfK8MfGqA2KvwcIBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"827f98b49732de844b00a158d89501c504249213dfdff91eb28241805d6e08eb","last_reissued_at":"2026-07-05T08:02:44.506578Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:44.506578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Amer Ghanem, Bharti Goel, Bokai Zhang, Mohammad Hasan Sarhan, Svetlana Petculescu","submitted_at":"2023-06-15T05:04:29Z","abstract_excerpt":"Automatic surgical phase recognition is one of the key technologies to support Video-Based Assessment (VBA) systems for surgical education. Utilizing temporal information is crucial for surgical phase recognition, hence various recent approaches extract frame-level features to conduct full video temporal modeling. For better temporal modeling, we propose SlowFast Temporal Modeling Network (SF-TMN) for surgical phase recognition that can not only achieve frame-level full video temporal modeling but also achieve segment-level full video temporal modeling. We employ a feature extraction network, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.08859","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/2306.08859/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":"2306.08859","created_at":"2026-07-05T08:02:44.506654+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.08859v1","created_at":"2026-07-05T08:02:44.506654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.08859","created_at":"2026-07-05T08:02:44.506654+00:00"},{"alias_kind":"pith_short_12","alias_value":"QJ7ZRNEXGLPI","created_at":"2026-07-05T08:02:44.506654+00:00"},{"alias_kind":"pith_short_16","alias_value":"QJ7ZRNEXGLPIISYA","created_at":"2026-07-05T08:02:44.506654+00:00"},{"alias_kind":"pith_short_8","alias_value":"QJ7ZRNEX","created_at":"2026-07-05T08:02:44.506654+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/QJ7ZRNEXGLPIISYAUFMNRFIBYU","json":"https://pith.science/pith/QJ7ZRNEXGLPIISYAUFMNRFIBYU.json","graph_json":"https://pith.science/api/pith-number/QJ7ZRNEXGLPIISYAUFMNRFIBYU/graph.json","events_json":"https://pith.science/api/pith-number/QJ7ZRNEXGLPIISYAUFMNRFIBYU/events.json","paper":"https://pith.science/paper/QJ7ZRNEX"},"agent_actions":{"view_html":"https://pith.science/pith/QJ7ZRNEXGLPIISYAUFMNRFIBYU","download_json":"https://pith.science/pith/QJ7ZRNEXGLPIISYAUFMNRFIBYU.json","view_paper":"https://pith.science/paper/QJ7ZRNEX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.08859&json=true","fetch_graph":"https://pith.science/api/pith-number/QJ7ZRNEXGLPIISYAUFMNRFIBYU/graph.json","fetch_events":"https://pith.science/api/pith-number/QJ7ZRNEXGLPIISYAUFMNRFIBYU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QJ7ZRNEXGLPIISYAUFMNRFIBYU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QJ7ZRNEXGLPIISYAUFMNRFIBYU/action/storage_attestation","attest_author":"https://pith.science/pith/QJ7ZRNEXGLPIISYAUFMNRFIBYU/action/author_attestation","sign_citation":"https://pith.science/pith/QJ7ZRNEXGLPIISYAUFMNRFIBYU/action/citation_signature","submit_replication":"https://pith.science/pith/QJ7ZRNEXGLPIISYAUFMNRFIBYU/action/replication_record"}},"created_at":"2026-07-05T08:02:44.506654+00:00","updated_at":"2026-07-05T08:02:44.506654+00:00"}