{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:RTSTVKUJVERGA7RPLFCYBQDFHR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5d12ef75ea0da2af8b1acf5ce71b23d061bbcd2f5f62f5b2ae236acfb544184f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-03T16:44:46Z","title_canon_sha256":"ca41308a33c013addfbb5d5e358611e8da14cc20d7f5f47a53f1852bbf7fdb19"},"schema_version":"1.0","source":{"id":"1704.00675","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.00675","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"arxiv_version","alias_value":"1704.00675v3","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.00675","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"pith_short_12","alias_value":"RTSTVKUJVERG","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RTSTVKUJVERGA7RP","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RTSTVKUJ","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:1f578e48e52fe0dcdadde551f6d2958c4adb69e263ffd3a2d7e80a3e50f9f327","target":"graph","created_at":"2026-05-18T03:32:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset, benchmark, and competition specifically designed for the task of video object segmentation."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The chosen videos and metrics sufficiently represent the diversity and difficulty of real-world video object segmentation scenarios."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"The 2017 DAVIS Challenge establishes a public dataset, evaluation metrics, and competition for video object segmentation."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"The 2017 DAVIS Challenge introduces a dataset, benchmark, and competition to advance video object segmentation."}],"snapshot_sha256":"2ca4982a192b2313e7eda58039503d72d7b134b257eeba23b5df91f0e26c4dc9"},"formal_canon":{"evidence_count":3,"snapshot_sha256":"95cb96821a914bf28aa07ad2bf3d07fcbbc3092dc45194e9926a2f392f42404c"},"paper":{"abstract_excerpt":"We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset, benchmark, and competition specifically designed for the task of video object segmentation. Following the footsteps of other successful initiatives, such as ILSVRC and PASCAL VOC, which established the avenue of research in the fields of scene classification and semantic segmentation, the DAVIS Challenge comprises a dataset, an evaluation methodology, and a public competition with a dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on the recent publication of DAVIS (Densely-Annotated","authors_text":"Alex Sorkine-Hornung, Federico Perazzi, Jordi Pont-Tuset, Luc Van Gool, Pablo Arbel\\'aez, Sergi Caelles","cross_cats":[],"headline":"The 2017 DAVIS Challenge introduces a dataset, benchmark, and competition to advance video object segmentation.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-03T16:44:46Z","title":"The 2017 DAVIS Challenge on Video Object Segmentation"},"references":{"count":17,"internal_anchors":0,"resolved_work":17,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"ImageNet Large Scale Visual Recognition Challenge","work_id":"552fef66-9973-48f7-b1d7-ccabbd78b649","year":2015},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results","work_id":"0f11c9db-9e95-47e1-9465-913772fb3c30","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"A benchmark dataset and evaluation methodology for video object segmentation","work_id":"d5bf746b-73c7-440c-904a-c4fead850a2c","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Microsoft COCO: Common Objects in Context","work_id":"ceb3505c-acbf-4176-98a7-d5b9c9cebb51","year":2014},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"One-shot video object segmentation","work_id":"488f1fa4-558c-4104-b88d-1af65801e27c","year":2017}],"snapshot_sha256":"79809375886052f5da6340aa0ab3fb0c1513b4f604e119ee9e7f83bb8f569106"},"source":{"id":"1704.00675","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-13T23:50:24.954342Z","id":"27b946f1-b766-40e6-9f58-fe1e6a105798","model_set":{"reader":"grok-4.3"},"one_line_summary":"The 2017 DAVIS Challenge establishes a public dataset, evaluation metrics, and competition for video object segmentation.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"The 2017 DAVIS Challenge introduces a dataset, benchmark, and competition to advance video object segmentation.","strongest_claim":"We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset, benchmark, and competition specifically designed for the task of video object segmentation.","weakest_assumption":"The chosen videos and metrics sufficiently represent the diversity and difficulty of real-world video object segmentation scenarios."}},"verdict_id":"27b946f1-b766-40e6-9f58-fe1e6a105798"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:17944d870be9c01976eb96c9c37445dfc9afab090a22b18bbff7d83f6fd0961a","target":"record","created_at":"2026-05-18T03:32:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5d12ef75ea0da2af8b1acf5ce71b23d061bbcd2f5f62f5b2ae236acfb544184f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-03T16:44:46Z","title_canon_sha256":"ca41308a33c013addfbb5d5e358611e8da14cc20d7f5f47a53f1852bbf7fdb19"},"schema_version":"1.0","source":{"id":"1704.00675","kind":"arxiv","version":3}},"canonical_sha256":"8ce53aaa89a922607e2f594580c0653c5f747735e02562a7c8de6acd1b6f0286","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ce53aaa89a922607e2f594580c0653c5f747735e02562a7c8de6acd1b6f0286","first_computed_at":"2026-05-18T03:32:29.312678Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:32:29.312678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tavLWRklk4YNrpbs461NXp3WfZbjudoV9AVJHlbcWWvf5zq2Xp2S/fNBTpHgkQm6ZRXCzEDV2GeChzqzzgF9CQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:32:29.313591Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.00675","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:17944d870be9c01976eb96c9c37445dfc9afab090a22b18bbff7d83f6fd0961a","sha256:1f578e48e52fe0dcdadde551f6d2958c4adb69e263ffd3a2d7e80a3e50f9f327"],"state_sha256":"4e3876772703ecd4f457c0b8826e1134e580835b29c36a3e5eadf22d96d5e468"}