{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:VONKVQ5I4DVR23YRUXFWMTPJYP","short_pith_number":"pith:VONKVQ5I","schema_version":"1.0","canonical_sha256":"ab9aaac3a8e0eb1d6f11a5cb664de9c3cbb579e554eda796c709d75c728050a3","source":{"kind":"arxiv","id":"1809.01123","version":1},"attestation_state":"computed","paper":{"title":"VideoMatch: Matching based Video Object Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexander G. Schwing, Jia-Bin Huang, Yuan-Ting Hu","submitted_at":"2018-09-04T17:59:53Z","abstract_excerpt":"Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task using deep nets to achieve appealing state-of-the-art performance. Due to the formulation as a prediction task, most of these methods require fine-tuning during test time, such that the deep nets memorize the appearance of the objects of interest in the given video. However, fine-tuning is time-consuming and computationally expensive, hence the algorithms are far from real time. To address this issue, we develop a nov"},"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":"1809.01123","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-04T17:59:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"120e42c50bbc581f1c1b12bd43d8d764580bc7591162700ffa4abcf7fff537b5","abstract_canon_sha256":"eb5eeced0d28f61cd553eafe50126b55b09ea134bd212358ed2dbcc15d97727b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:31.723598Z","signature_b64":"E4w/SQmgfcmcLF99G9MLVy98YrU+VeFxXG1DPq4+DiUQU3L9FTgUhQQqBOE0ziWkcxRPT1YSTI2PkdTWq+W0Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab9aaac3a8e0eb1d6f11a5cb664de9c3cbb579e554eda796c709d75c728050a3","last_reissued_at":"2026-05-18T00:06:31.723153Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:31.723153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VideoMatch: Matching based Video Object Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexander G. Schwing, Jia-Bin Huang, Yuan-Ting Hu","submitted_at":"2018-09-04T17:59:53Z","abstract_excerpt":"Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task using deep nets to achieve appealing state-of-the-art performance. Due to the formulation as a prediction task, most of these methods require fine-tuning during test time, such that the deep nets memorize the appearance of the objects of interest in the given video. However, fine-tuning is time-consuming and computationally expensive, hence the algorithms are far from real time. To address this issue, we develop a nov"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.01123","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":"1809.01123","created_at":"2026-05-18T00:06:31.723220+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.01123v1","created_at":"2026-05-18T00:06:31.723220+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.01123","created_at":"2026-05-18T00:06:31.723220+00:00"},{"alias_kind":"pith_short_12","alias_value":"VONKVQ5I4DVR","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"VONKVQ5I4DVR23YR","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"VONKVQ5I","created_at":"2026-05-18T12:32:59.047623+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2408.00714","citing_title":"SAM 2: Segment Anything in Images and Videos","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP","json":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP.json","graph_json":"https://pith.science/api/pith-number/VONKVQ5I4DVR23YRUXFWMTPJYP/graph.json","events_json":"https://pith.science/api/pith-number/VONKVQ5I4DVR23YRUXFWMTPJYP/events.json","paper":"https://pith.science/paper/VONKVQ5I"},"agent_actions":{"view_html":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP","download_json":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP.json","view_paper":"https://pith.science/paper/VONKVQ5I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.01123&json=true","fetch_graph":"https://pith.science/api/pith-number/VONKVQ5I4DVR23YRUXFWMTPJYP/graph.json","fetch_events":"https://pith.science/api/pith-number/VONKVQ5I4DVR23YRUXFWMTPJYP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP/action/storage_attestation","attest_author":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP/action/author_attestation","sign_citation":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP/action/citation_signature","submit_replication":"https://pith.science/pith/VONKVQ5I4DVR23YRUXFWMTPJYP/action/replication_record"}},"created_at":"2026-05-18T00:06:31.723220+00:00","updated_at":"2026-05-18T00:06:31.723220+00:00"}