{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:O6V2D5XXVMGYM7BRL2RFXFNGJI","short_pith_number":"pith:O6V2D5XX","schema_version":"1.0","canonical_sha256":"77aba1f6f7ab0d867c315ea25b95a64a3fe28c580385ac6a1a46fc50f994c6f8","source":{"kind":"arxiv","id":"1811.00982","version":2},"attestation_state":"computed","paper":{"title":"The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Kolesnikov, Alina Kuznetsova, Hassan Rom, Ivan Krasin, Jasper Uijlings, Jordi Pont-Tuset, Matteo Malloci, Neil Alldrin, Shahab Kamali, Stefan Popov, Tom Duerig, Vittorio Ferrari","submitted_at":"2018-11-02T16:58:28Z","abstract_excerpt":"We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship a"},"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":"1811.00982","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-02T16:58:28Z","cross_cats_sorted":[],"title_canon_sha256":"50772996031eecde0bbb88153981d9c8564859682b57f2cc418ffeb8556da558","abstract_canon_sha256":"2017bd7447de627a7d2b4e4d31ec4ffafe0196f4add2665f1902f5fab5c8d00e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:50:23.941443Z","signature_b64":"aL2B3Tl9JfC9MHVYWOSwVUuqq31H1SP5EM/FZJ5G2RoWnRzkFr0urUP67MYs1z7hpcmw3BI+VaXj4UG6MG3oBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77aba1f6f7ab0d867c315ea25b95a64a3fe28c580385ac6a1a46fc50f994c6f8","last_reissued_at":"2026-07-05T00:50:23.940920Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:50:23.940920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Kolesnikov, Alina Kuznetsova, Hassan Rom, Ivan Krasin, Jasper Uijlings, Jordi Pont-Tuset, Matteo Malloci, Neil Alldrin, Shahab Kamali, Stefan Popov, Tom Duerig, Vittorio Ferrari","submitted_at":"2018-11-02T16:58:28Z","abstract_excerpt":"We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00982","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1811.00982/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":"1811.00982","created_at":"2026-07-05T00:50:23.940987+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.00982v2","created_at":"2026-07-05T00:50:23.940987+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00982","created_at":"2026-07-05T00:50:23.940987+00:00"},{"alias_kind":"pith_short_12","alias_value":"O6V2D5XXVMGY","created_at":"2026-07-05T00:50:23.940987+00:00"},{"alias_kind":"pith_short_16","alias_value":"O6V2D5XXVMGYM7BR","created_at":"2026-07-05T00:50:23.940987+00:00"},{"alias_kind":"pith_short_8","alias_value":"O6V2D5XX","created_at":"2026-07-05T00:50:23.940987+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.14966","citing_title":"MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"1907.01478","citing_title":"Obj-GloVe: Scene-Based Contextual Object Embedding","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"1907.07023","citing_title":"Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"1907.10902","citing_title":"Optuna: A Next-generation Hyperparameter Optimization Framework","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2302.11550","citing_title":"Scaling Robot Learning with Semantically Imagined Experience","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2112.10752","citing_title":"High-Resolution Image Synthesis with Latent Diffusion Models","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI","json":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI.json","graph_json":"https://pith.science/api/pith-number/O6V2D5XXVMGYM7BRL2RFXFNGJI/graph.json","events_json":"https://pith.science/api/pith-number/O6V2D5XXVMGYM7BRL2RFXFNGJI/events.json","paper":"https://pith.science/paper/O6V2D5XX"},"agent_actions":{"view_html":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI","download_json":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI.json","view_paper":"https://pith.science/paper/O6V2D5XX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.00982&json=true","fetch_graph":"https://pith.science/api/pith-number/O6V2D5XXVMGYM7BRL2RFXFNGJI/graph.json","fetch_events":"https://pith.science/api/pith-number/O6V2D5XXVMGYM7BRL2RFXFNGJI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI/action/storage_attestation","attest_author":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI/action/author_attestation","sign_citation":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI/action/citation_signature","submit_replication":"https://pith.science/pith/O6V2D5XXVMGYM7BRL2RFXFNGJI/action/replication_record"}},"created_at":"2026-07-05T00:50:23.940987+00:00","updated_at":"2026-07-05T00:50:23.940987+00:00"}