{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:3FBUAFZ7MOQNOQSKQDZCYVL4DJ","short_pith_number":"pith:3FBUAFZ7","schema_version":"1.0","canonical_sha256":"d94340173f63a0d7424a80f22c557c1a67a796df4a887472c107098b3c48aab9","source":{"kind":"arxiv","id":"1904.08630","version":1},"attestation_state":"computed","paper":{"title":"Discriminative Online Learning for Fast Video Object Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas Robinson, Fahad Shahbaz Khan, Felix J\\\"aremo Lawin, Martin Danelljan, Michael Felsberg","submitted_at":"2019-04-18T08:11:07Z","abstract_excerpt":"We address the highly challenging problem of video object segmentation. Given only the initial mask, the task is to segment the target in the subsequent frames. In order to effectively handle appearance changes and similar background objects, a robust representation of the target is required. Previous approaches either rely on fine-tuning a segmentation network on the first frame, or employ generative appearance models. Although partially successful, these methods often suffer from impractically low frame rates or unsatisfactory robustness.\n  We propose a novel approach, based on a dedicated t"},"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":"1904.08630","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-18T08:11:07Z","cross_cats_sorted":[],"title_canon_sha256":"ef48818cd9685e6807a1a96a2edc4749b15a054febf12976d16fa36cf7a84887","abstract_canon_sha256":"efbe2d190e4bfc2be22c92f0a879783da925f2ff1f0204202941c7186e28ad5c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:13.623824Z","signature_b64":"95bv01B8QW3MpKDqc73//fEyQiGZU1kHj2HjAjh9oWucB21E2vWvIFYh8SjppPZVnkV5hgpp2s7Nrt+Wo+KACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d94340173f63a0d7424a80f22c557c1a67a796df4a887472c107098b3c48aab9","last_reissued_at":"2026-05-17T23:48:13.623217Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:13.623217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Discriminative Online Learning for Fast Video Object Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas Robinson, Fahad Shahbaz Khan, Felix J\\\"aremo Lawin, Martin Danelljan, Michael Felsberg","submitted_at":"2019-04-18T08:11:07Z","abstract_excerpt":"We address the highly challenging problem of video object segmentation. Given only the initial mask, the task is to segment the target in the subsequent frames. In order to effectively handle appearance changes and similar background objects, a robust representation of the target is required. Previous approaches either rely on fine-tuning a segmentation network on the first frame, or employ generative appearance models. Although partially successful, these methods often suffer from impractically low frame rates or unsatisfactory robustness.\n  We propose a novel approach, based on a dedicated t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08630","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":"1904.08630","created_at":"2026-05-17T23:48:13.623296+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.08630v1","created_at":"2026-05-17T23:48:13.623296+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.08630","created_at":"2026-05-17T23:48:13.623296+00:00"},{"alias_kind":"pith_short_12","alias_value":"3FBUAFZ7MOQN","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"3FBUAFZ7MOQNOQSK","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"3FBUAFZ7","created_at":"2026-05-18T12:33:07.085635+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/3FBUAFZ7MOQNOQSKQDZCYVL4DJ","json":"https://pith.science/pith/3FBUAFZ7MOQNOQSKQDZCYVL4DJ.json","graph_json":"https://pith.science/api/pith-number/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/graph.json","events_json":"https://pith.science/api/pith-number/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/events.json","paper":"https://pith.science/paper/3FBUAFZ7"},"agent_actions":{"view_html":"https://pith.science/pith/3FBUAFZ7MOQNOQSKQDZCYVL4DJ","download_json":"https://pith.science/pith/3FBUAFZ7MOQNOQSKQDZCYVL4DJ.json","view_paper":"https://pith.science/paper/3FBUAFZ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.08630&json=true","fetch_graph":"https://pith.science/api/pith-number/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/graph.json","fetch_events":"https://pith.science/api/pith-number/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/action/storage_attestation","attest_author":"https://pith.science/pith/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/action/author_attestation","sign_citation":"https://pith.science/pith/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/action/citation_signature","submit_replication":"https://pith.science/pith/3FBUAFZ7MOQNOQSKQDZCYVL4DJ/action/replication_record"}},"created_at":"2026-05-17T23:48:13.623296+00:00","updated_at":"2026-05-17T23:48:13.623296+00:00"}