{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:K2LJUTJGG2BUL5MGSW2K4QCC46","short_pith_number":"pith:K2LJUTJG","schema_version":"1.0","canonical_sha256":"56969a4d26368345f58695b4ae4042e7926434ae34039d75ac0683161e3b9fff","source":{"kind":"arxiv","id":"2109.13593","version":1},"attestation_state":"computed","paper":{"title":"Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Jiacheng Wang, Jing Qin, Liansheng Wang, Pheng-Ann Heng, Shuntian Cai, Yueming Jin","submitted_at":"2021-09-28T10:10:14Z","abstract_excerpt":"Performing a real-time and accurate instrument segmentation from videos is of great significance for improving the performance of robotic-assisted surgery. We identify two important clues for surgical instrument perception, including local temporal dependency from adjacent frames and global semantic correlation in long-range duration. However, most existing works perform segmentation purely using visual cues in a single frame. Optical flow is just used to model the motion between only two frames and brings heavy computational cost. We propose a novel dual-memory network (DMNet) to wisely relat"},"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":"2109.13593","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-28T10:10:14Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"ec92a57598ee50130bc6f0b6f9b52433bc987a26c3cd222ff07952fe396dae3a","abstract_canon_sha256":"b2eaf29fbda77e12b4c7d96c1b8faf7ba1369a6f24b7e07e986b06112099a158"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:18:04.370203Z","signature_b64":"VwelhiqLV8DZrp70imrcC0EutqBtT3XFjORuyYCkewA2k8zfQ9B6K0VbDGu5TgXQnzP/dCHPZJqH7Q6N7vMPBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56969a4d26368345f58695b4ae4042e7926434ae34039d75ac0683161e3b9fff","last_reissued_at":"2026-07-05T03:18:04.369852Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:18:04.369852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Jiacheng Wang, Jing Qin, Liansheng Wang, Pheng-Ann Heng, Shuntian Cai, Yueming Jin","submitted_at":"2021-09-28T10:10:14Z","abstract_excerpt":"Performing a real-time and accurate instrument segmentation from videos is of great significance for improving the performance of robotic-assisted surgery. We identify two important clues for surgical instrument perception, including local temporal dependency from adjacent frames and global semantic correlation in long-range duration. However, most existing works perform segmentation purely using visual cues in a single frame. Optical flow is just used to model the motion between only two frames and brings heavy computational cost. We propose a novel dual-memory network (DMNet) to wisely relat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.13593","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/2109.13593/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":"2109.13593","created_at":"2026-07-05T03:18:04.369912+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.13593v1","created_at":"2026-07-05T03:18:04.369912+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.13593","created_at":"2026-07-05T03:18:04.369912+00:00"},{"alias_kind":"pith_short_12","alias_value":"K2LJUTJGG2BU","created_at":"2026-07-05T03:18:04.369912+00:00"},{"alias_kind":"pith_short_16","alias_value":"K2LJUTJGG2BUL5MG","created_at":"2026-07-05T03:18:04.369912+00:00"},{"alias_kind":"pith_short_8","alias_value":"K2LJUTJG","created_at":"2026-07-05T03:18:04.369912+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/K2LJUTJGG2BUL5MGSW2K4QCC46","json":"https://pith.science/pith/K2LJUTJGG2BUL5MGSW2K4QCC46.json","graph_json":"https://pith.science/api/pith-number/K2LJUTJGG2BUL5MGSW2K4QCC46/graph.json","events_json":"https://pith.science/api/pith-number/K2LJUTJGG2BUL5MGSW2K4QCC46/events.json","paper":"https://pith.science/paper/K2LJUTJG"},"agent_actions":{"view_html":"https://pith.science/pith/K2LJUTJGG2BUL5MGSW2K4QCC46","download_json":"https://pith.science/pith/K2LJUTJGG2BUL5MGSW2K4QCC46.json","view_paper":"https://pith.science/paper/K2LJUTJG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.13593&json=true","fetch_graph":"https://pith.science/api/pith-number/K2LJUTJGG2BUL5MGSW2K4QCC46/graph.json","fetch_events":"https://pith.science/api/pith-number/K2LJUTJGG2BUL5MGSW2K4QCC46/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K2LJUTJGG2BUL5MGSW2K4QCC46/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K2LJUTJGG2BUL5MGSW2K4QCC46/action/storage_attestation","attest_author":"https://pith.science/pith/K2LJUTJGG2BUL5MGSW2K4QCC46/action/author_attestation","sign_citation":"https://pith.science/pith/K2LJUTJGG2BUL5MGSW2K4QCC46/action/citation_signature","submit_replication":"https://pith.science/pith/K2LJUTJGG2BUL5MGSW2K4QCC46/action/replication_record"}},"created_at":"2026-07-05T03:18:04.369912+00:00","updated_at":"2026-07-05T03:18:04.369912+00:00"}