{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PQLA3VY722NK7I4ATN4O2QWWEL","short_pith_number":"pith:PQLA3VY7","schema_version":"1.0","canonical_sha256":"7c160dd71fd69aafa3809b78ed42d622d7ddfe2a0339a00d13e7b36250d0afb2","source":{"kind":"arxiv","id":"1810.11981","version":3},"attestation_state":"computed","paper":{"title":"GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kaiqi Huang, Lianghua Huang, Xin Zhao","submitted_at":"2018-10-29T07:22:46Z","abstract_excerpt":"We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts. The contributions of this paper are summarized in the following: (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep tr"},"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":"1810.11981","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-29T07:22:46Z","cross_cats_sorted":[],"title_canon_sha256":"05fe0230b92425ae64862f42d6ecf8c06e8781c7f02ce5611a4796630f609c74","abstract_canon_sha256":"48d443c50086b6acbb16cfefc6e4d5bb05c9610b040a7cb77c1ffeac395b169d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:27:14.898120Z","signature_b64":"xkh7D3ls/hvjtHVNiRp9qH9nUsKL3e0BJSVtPL5WuYs8cMJf9NakxU3wjmSG9w3u/kEYbPQAp9xWt6ZcB1BzDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c160dd71fd69aafa3809b78ed42d622d7ddfe2a0339a00d13e7b36250d0afb2","last_reissued_at":"2026-07-05T00:27:14.897555Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:27:14.897555Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kaiqi Huang, Lianghua Huang, Xin Zhao","submitted_at":"2018-10-29T07:22:46Z","abstract_excerpt":"We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts. The contributions of this paper are summarized in the following: (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11981","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1810.11981/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1810.11981","created_at":"2026-07-05T00:27:14.897617+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.11981v3","created_at":"2026-07-05T00:27:14.897617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11981","created_at":"2026-07-05T00:27:14.897617+00:00"},{"alias_kind":"pith_short_12","alias_value":"PQLA3VY722NK","created_at":"2026-07-05T00:27:14.897617+00:00"},{"alias_kind":"pith_short_16","alias_value":"PQLA3VY722NK7I4A","created_at":"2026-07-05T00:27:14.897617+00:00"},{"alias_kind":"pith_short_8","alias_value":"PQLA3VY7","created_at":"2026-07-05T00:27:14.897617+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.11714","citing_title":"Multi-Modal Fusion for End-to-End RGB-T Tracking","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL","json":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL.json","graph_json":"https://pith.science/api/pith-number/PQLA3VY722NK7I4ATN4O2QWWEL/graph.json","events_json":"https://pith.science/api/pith-number/PQLA3VY722NK7I4ATN4O2QWWEL/events.json","paper":"https://pith.science/paper/PQLA3VY7"},"agent_actions":{"view_html":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL","download_json":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL.json","view_paper":"https://pith.science/paper/PQLA3VY7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.11981&json=true","fetch_graph":"https://pith.science/api/pith-number/PQLA3VY722NK7I4ATN4O2QWWEL/graph.json","fetch_events":"https://pith.science/api/pith-number/PQLA3VY722NK7I4ATN4O2QWWEL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL/action/storage_attestation","attest_author":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL/action/author_attestation","sign_citation":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL/action/citation_signature","submit_replication":"https://pith.science/pith/PQLA3VY722NK7I4ATN4O2QWWEL/action/replication_record"}},"created_at":"2026-07-05T00:27:14.897617+00:00","updated_at":"2026-07-05T00:27:14.897617+00:00"}