{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2ZV7CZ7EY5UHVIBW3YLLDJVVBC","short_pith_number":"pith:2ZV7CZ7E","schema_version":"1.0","canonical_sha256":"d66bf167e4c7687aa036de16b1a6b508af72636ba3f471adea6b0ff359a56e65","source":{"kind":"arxiv","id":"1811.07958","version":2},"attestation_state":"computed","paper":{"title":"Tukey-Inspired Video Object Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Brent A. Griffin, Jason J. Corso","submitted_at":"2018-11-19T20:15:27Z","abstract_excerpt":"We investigate the problem of strictly unsupervised video object segmentation, i.e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset. We find foreground objects in low-level vision data using a John Tukey-inspired measure of \"outlierness\". This Tukey-inspired measure also estimates the reliability of each data source as video characteristics change (e.g., a camera starts moving). The proposed method achieves state-of-the-art results for strictly unsupervised video object segmentation on the challenging DAVI"},"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":"1811.07958","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T20:15:27Z","cross_cats_sorted":[],"title_canon_sha256":"fc89fe8bcac311435b211813b0be772fc4ef12acfa0594143b001eb9590a8bc7","abstract_canon_sha256":"cba640ed5511012d46dacc26cc4ba45d9d10a5a2e4b83499bf9358c446fa1b00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:31.973066Z","signature_b64":"bOLfZuBGlkSpHKSjB+gKcRi7ONuGOmTBZav7zT6Z3r46EIbeQV5G7IHFxsHQdi9mM5l7inm+AWOUfCIY3q1RBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d66bf167e4c7687aa036de16b1a6b508af72636ba3f471adea6b0ff359a56e65","last_reissued_at":"2026-05-17T23:59:31.972471Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:31.972471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tukey-Inspired Video Object Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Brent A. Griffin, Jason J. Corso","submitted_at":"2018-11-19T20:15:27Z","abstract_excerpt":"We investigate the problem of strictly unsupervised video object segmentation, i.e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset. We find foreground objects in low-level vision data using a John Tukey-inspired measure of \"outlierness\". This Tukey-inspired measure also estimates the reliability of each data source as video characteristics change (e.g., a camera starts moving). The proposed method achieves state-of-the-art results for strictly unsupervised video object segmentation on the challenging DAVI"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07958","kind":"arxiv","version":2},"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":"1811.07958","created_at":"2026-05-17T23:59:31.972569+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.07958v2","created_at":"2026-05-17T23:59:31.972569+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07958","created_at":"2026-05-17T23:59:31.972569+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZV7CZ7EY5UH","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZV7CZ7EY5UHVIBW","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZV7CZ7E","created_at":"2026-05-18T12:32:02.567920+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/2ZV7CZ7EY5UHVIBW3YLLDJVVBC","json":"https://pith.science/pith/2ZV7CZ7EY5UHVIBW3YLLDJVVBC.json","graph_json":"https://pith.science/api/pith-number/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/graph.json","events_json":"https://pith.science/api/pith-number/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/events.json","paper":"https://pith.science/paper/2ZV7CZ7E"},"agent_actions":{"view_html":"https://pith.science/pith/2ZV7CZ7EY5UHVIBW3YLLDJVVBC","download_json":"https://pith.science/pith/2ZV7CZ7EY5UHVIBW3YLLDJVVBC.json","view_paper":"https://pith.science/paper/2ZV7CZ7E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.07958&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/action/storage_attestation","attest_author":"https://pith.science/pith/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/action/author_attestation","sign_citation":"https://pith.science/pith/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/action/citation_signature","submit_replication":"https://pith.science/pith/2ZV7CZ7EY5UHVIBW3YLLDJVVBC/action/replication_record"}},"created_at":"2026-05-17T23:59:31.972569+00:00","updated_at":"2026-05-17T23:59:31.972569+00:00"}