{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VBPETTCZ42EEHTMCZBT2RHX7JB","short_pith_number":"pith:VBPETTCZ","schema_version":"1.0","canonical_sha256":"a85e49cc59e68843cd82c867a89eff48653ecccf786bd522b9c0fcfecf028c7e","source":{"kind":"arxiv","id":"1602.04906","version":1},"attestation_state":"computed","paper":{"title":"Segmentation Rectification for Video Cutout via One-Class Structured Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jue Wang, Junyan Wang, Kun Zhou, Sai-Kit Yeung","submitted_at":"2016-02-16T04:31:20Z","abstract_excerpt":"Recent works on interactive video object cutout mainly focus on designing dynamic foreground-background (FB) classifiers for segmentation propagation. However, the research on optimally removing errors from the FB classification is sparse, and the errors often accumulate rapidly, causing significant errors in the propagated frames. In this work, we take the initial steps to addressing this problem, and we call this new task \\emph{segmentation rectification}. Our key observation is that the possibly asymmetrically distributed false positive and false negative errors were handled equally in the "},"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":"1602.04906","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-16T04:31:20Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"dd7e7e660e6052f78ac200a4d602b0f5a7989ddbd6208bb014a654bf9338b4db","abstract_canon_sha256":"c8f5c8b19075da6ebfd4160bb6ea51cd01b5a08eb40ef7304fa8191bc7213411"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:37.895060Z","signature_b64":"F696PAiNqJQYMxeXR9lhV8wrEMjl2++42X/aRMsP53pb9Y3AHkYljCBML8/rdi4d7tE/ig5MVi9O/tcePSFRCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a85e49cc59e68843cd82c867a89eff48653ecccf786bd522b9c0fcfecf028c7e","last_reissued_at":"2026-05-18T01:20:37.894678Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:37.894678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Segmentation Rectification for Video Cutout via One-Class Structured Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jue Wang, Junyan Wang, Kun Zhou, Sai-Kit Yeung","submitted_at":"2016-02-16T04:31:20Z","abstract_excerpt":"Recent works on interactive video object cutout mainly focus on designing dynamic foreground-background (FB) classifiers for segmentation propagation. However, the research on optimally removing errors from the FB classification is sparse, and the errors often accumulate rapidly, causing significant errors in the propagated frames. In this work, we take the initial steps to addressing this problem, and we call this new task \\emph{segmentation rectification}. Our key observation is that the possibly asymmetrically distributed false positive and false negative errors were handled equally in the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04906","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":""},"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":"1602.04906","created_at":"2026-05-18T01:20:37.894735+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.04906v1","created_at":"2026-05-18T01:20:37.894735+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04906","created_at":"2026-05-18T01:20:37.894735+00:00"},{"alias_kind":"pith_short_12","alias_value":"VBPETTCZ42EE","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VBPETTCZ42EEHTMC","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VBPETTCZ","created_at":"2026-05-18T12:30:48.956258+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/VBPETTCZ42EEHTMCZBT2RHX7JB","json":"https://pith.science/pith/VBPETTCZ42EEHTMCZBT2RHX7JB.json","graph_json":"https://pith.science/api/pith-number/VBPETTCZ42EEHTMCZBT2RHX7JB/graph.json","events_json":"https://pith.science/api/pith-number/VBPETTCZ42EEHTMCZBT2RHX7JB/events.json","paper":"https://pith.science/paper/VBPETTCZ"},"agent_actions":{"view_html":"https://pith.science/pith/VBPETTCZ42EEHTMCZBT2RHX7JB","download_json":"https://pith.science/pith/VBPETTCZ42EEHTMCZBT2RHX7JB.json","view_paper":"https://pith.science/paper/VBPETTCZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.04906&json=true","fetch_graph":"https://pith.science/api/pith-number/VBPETTCZ42EEHTMCZBT2RHX7JB/graph.json","fetch_events":"https://pith.science/api/pith-number/VBPETTCZ42EEHTMCZBT2RHX7JB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VBPETTCZ42EEHTMCZBT2RHX7JB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VBPETTCZ42EEHTMCZBT2RHX7JB/action/storage_attestation","attest_author":"https://pith.science/pith/VBPETTCZ42EEHTMCZBT2RHX7JB/action/author_attestation","sign_citation":"https://pith.science/pith/VBPETTCZ42EEHTMCZBT2RHX7JB/action/citation_signature","submit_replication":"https://pith.science/pith/VBPETTCZ42EEHTMCZBT2RHX7JB/action/replication_record"}},"created_at":"2026-05-18T01:20:37.894735+00:00","updated_at":"2026-05-18T01:20:37.894735+00:00"}