{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AWVPMCMLB3TWO5QSPN5OR7X2JV","short_pith_number":"pith:AWVPMCML","schema_version":"1.0","canonical_sha256":"05aaf6098b0ee76776127b7ae8fefa4d7185b1c69f62097bf1516c95bb1f3256","source":{"kind":"arxiv","id":"1904.08189","version":3},"attestation_state":"computed","paper":{"title":"CenterNet: Keypoint Triplets for Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Honggang Qi, Kaiwen Duan, Lingxi Xie, Qingming Huang, Qi Tian, Song Bai","submitted_at":"2019-04-17T11:20:01Z","abstract_excerpt":"In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions. This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules named cas"},"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":"1904.08189","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-17T11:20:01Z","cross_cats_sorted":[],"title_canon_sha256":"a9e789297f38b360e3592ef19b132e672e786dc13262beb87109b831834c3dd8","abstract_canon_sha256":"87610c9303cad3666d5853d9bdc6451b9a70a0b7dbce0779247f7e574710036a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:08.917152Z","signature_b64":"Px6OvC2anSdGG2VeVOBSVTGvZwKoeet0H8DJdgeN4zUObX9015mEW1lLKuLUJ/VCD6Os44XNC3urvzHQQJ6uDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05aaf6098b0ee76776127b7ae8fefa4d7185b1c69f62097bf1516c95bb1f3256","last_reissued_at":"2026-05-17T23:48:08.916437Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:08.916437Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CenterNet: Keypoint Triplets for Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Honggang Qi, Kaiwen Duan, Lingxi Xie, Qingming Huang, Qi Tian, Song Bai","submitted_at":"2019-04-17T11:20:01Z","abstract_excerpt":"In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions. This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules named cas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08189","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":""},"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":"1904.08189","created_at":"2026-05-17T23:48:08.916558+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.08189v3","created_at":"2026-05-17T23:48:08.916558+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.08189","created_at":"2026-05-17T23:48:08.916558+00:00"},{"alias_kind":"pith_short_12","alias_value":"AWVPMCMLB3TW","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"AWVPMCMLB3TWO5QS","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"AWVPMCML","created_at":"2026-05-18T12:33:12.712433+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/AWVPMCMLB3TWO5QSPN5OR7X2JV","json":"https://pith.science/pith/AWVPMCMLB3TWO5QSPN5OR7X2JV.json","graph_json":"https://pith.science/api/pith-number/AWVPMCMLB3TWO5QSPN5OR7X2JV/graph.json","events_json":"https://pith.science/api/pith-number/AWVPMCMLB3TWO5QSPN5OR7X2JV/events.json","paper":"https://pith.science/paper/AWVPMCML"},"agent_actions":{"view_html":"https://pith.science/pith/AWVPMCMLB3TWO5QSPN5OR7X2JV","download_json":"https://pith.science/pith/AWVPMCMLB3TWO5QSPN5OR7X2JV.json","view_paper":"https://pith.science/paper/AWVPMCML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.08189&json=true","fetch_graph":"https://pith.science/api/pith-number/AWVPMCMLB3TWO5QSPN5OR7X2JV/graph.json","fetch_events":"https://pith.science/api/pith-number/AWVPMCMLB3TWO5QSPN5OR7X2JV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AWVPMCMLB3TWO5QSPN5OR7X2JV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AWVPMCMLB3TWO5QSPN5OR7X2JV/action/storage_attestation","attest_author":"https://pith.science/pith/AWVPMCMLB3TWO5QSPN5OR7X2JV/action/author_attestation","sign_citation":"https://pith.science/pith/AWVPMCMLB3TWO5QSPN5OR7X2JV/action/citation_signature","submit_replication":"https://pith.science/pith/AWVPMCMLB3TWO5QSPN5OR7X2JV/action/replication_record"}},"created_at":"2026-05-17T23:48:08.916558+00:00","updated_at":"2026-05-17T23:48:08.916558+00:00"}