{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:DKBDUMUM2D5I4KU3OWF7KFHDNB","short_pith_number":"pith:DKBDUMUM","schema_version":"1.0","canonical_sha256":"1a823a328cd0fa8e2a9b758bf514e368763f5ec7f5a0f33e03c4b4beac24c46a","source":{"kind":"arxiv","id":"2607.00752","version":1},"attestation_state":"computed","paper":{"title":"GKDT: General Keypoint Detection Transformer","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Changsheng Lu, Haokun Gui, Harry Yang, Jiaya Jia, Jie Yang, Rong Wang, Yuxin Chen","submitted_at":"2026-07-01T10:37:29Z","abstract_excerpt":"With the emergence of various pre-trained vision and language models, computer vision is shifting from narrow-domain to open-domain recognition. The construction of a more powerful yet general keypoint detection (GKD) model to support diverse tasks has become increasingly important in the field. To this end, we firstly present a large-scale unified keypoint dataset called MegaKPT. The dataset is composed of over 1.3 million diverse object instances from twenty-nine existing datasets, and enjoys high-quality unified annotations with keypoint text descriptions. Based on MegaKPT, we develop GKDT,"},"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":"2607.00752","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T10:37:29Z","cross_cats_sorted":[],"title_canon_sha256":"88b7c9479f99728dc8eec4c2480ad8454f251bce5de81f2698a3bc0fe2940f91","abstract_canon_sha256":"815ad028f404ac51355f15e5533b4a3c85d8a86515d90fc66be8f4fe789f0f22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:17:54.485159Z","signature_b64":"QVUPiGzzHH9zv9Tw9D00VfTB4nstRMU2NXG/+P4v1IpduVQqYmyl36FvnbzGO/rZh8q9R3anfKxfT3ydQLMsAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a823a328cd0fa8e2a9b758bf514e368763f5ec7f5a0f33e03c4b4beac24c46a","last_reissued_at":"2026-07-02T01:17:54.484685Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:17:54.484685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GKDT: General Keypoint Detection Transformer","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Changsheng Lu, Haokun Gui, Harry Yang, Jiaya Jia, Jie Yang, Rong Wang, Yuxin Chen","submitted_at":"2026-07-01T10:37:29Z","abstract_excerpt":"With the emergence of various pre-trained vision and language models, computer vision is shifting from narrow-domain to open-domain recognition. The construction of a more powerful yet general keypoint detection (GKD) model to support diverse tasks has become increasingly important in the field. To this end, we firstly present a large-scale unified keypoint dataset called MegaKPT. The dataset is composed of over 1.3 million diverse object instances from twenty-nine existing datasets, and enjoys high-quality unified annotations with keypoint text descriptions. Based on MegaKPT, we develop GKDT,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00752","kind":"arxiv","version":1},"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/2607.00752/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":"2607.00752","created_at":"2026-07-02T01:17:54.484754+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.00752v1","created_at":"2026-07-02T01:17:54.484754+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00752","created_at":"2026-07-02T01:17:54.484754+00:00"},{"alias_kind":"pith_short_12","alias_value":"DKBDUMUM2D5I","created_at":"2026-07-02T01:17:54.484754+00:00"},{"alias_kind":"pith_short_16","alias_value":"DKBDUMUM2D5I4KU3","created_at":"2026-07-02T01:17:54.484754+00:00"},{"alias_kind":"pith_short_8","alias_value":"DKBDUMUM","created_at":"2026-07-02T01:17:54.484754+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/DKBDUMUM2D5I4KU3OWF7KFHDNB","json":"https://pith.science/pith/DKBDUMUM2D5I4KU3OWF7KFHDNB.json","graph_json":"https://pith.science/api/pith-number/DKBDUMUM2D5I4KU3OWF7KFHDNB/graph.json","events_json":"https://pith.science/api/pith-number/DKBDUMUM2D5I4KU3OWF7KFHDNB/events.json","paper":"https://pith.science/paper/DKBDUMUM"},"agent_actions":{"view_html":"https://pith.science/pith/DKBDUMUM2D5I4KU3OWF7KFHDNB","download_json":"https://pith.science/pith/DKBDUMUM2D5I4KU3OWF7KFHDNB.json","view_paper":"https://pith.science/paper/DKBDUMUM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.00752&json=true","fetch_graph":"https://pith.science/api/pith-number/DKBDUMUM2D5I4KU3OWF7KFHDNB/graph.json","fetch_events":"https://pith.science/api/pith-number/DKBDUMUM2D5I4KU3OWF7KFHDNB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DKBDUMUM2D5I4KU3OWF7KFHDNB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DKBDUMUM2D5I4KU3OWF7KFHDNB/action/storage_attestation","attest_author":"https://pith.science/pith/DKBDUMUM2D5I4KU3OWF7KFHDNB/action/author_attestation","sign_citation":"https://pith.science/pith/DKBDUMUM2D5I4KU3OWF7KFHDNB/action/citation_signature","submit_replication":"https://pith.science/pith/DKBDUMUM2D5I4KU3OWF7KFHDNB/action/replication_record"}},"created_at":"2026-07-02T01:17:54.484754+00:00","updated_at":"2026-07-02T01:17:54.484754+00:00"}