{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LH7HEY3TX6D5IPADQX2RU6PNZ3","short_pith_number":"pith:LH7HEY3T","schema_version":"1.0","canonical_sha256":"59fe726373bf87d43c0385f51a79edcec077e77c4f9ae2a37cee1d792b8fe634","source":{"kind":"arxiv","id":"2506.04737","version":2},"attestation_state":"computed","paper":{"title":"Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Angelica Aviles-Rivero, Carola-Bibiane Sch\\\"onlieb, Mikhail Kennerley, Robby T. Tan","submitted_at":"2025-06-05T08:16:15Z","abstract_excerpt":"Combining multiple object detection datasets offers a path to improved generalisation but is hindered by inconsistencies in class semantics and bounding box annotations. Some methods to address this assume shared label taxonomies and address only spatial inconsistencies; others require manual relabelling, or produce a unified label space, which may be unsuitable when a fixed target label space is required. We propose Label-Aligned Transfer (LAT), a label transfer framework that systematically projects annotations from diverse source datasets into the label space of a target dataset. LAT begins"},"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":"2506.04737","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-05T08:16:15Z","cross_cats_sorted":[],"title_canon_sha256":"62cecf304b30aaf2e4b244a58415075e837aee184eec3ced6c26af92338ec24c","abstract_canon_sha256":"6af236c9ed2d2b13bd154e045f6e871526313c955df7baa9f9df9dde3a3614ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:00.581138Z","signature_b64":"KkTNllhMshTMYGJUJXLOP3YqA9z2Nz8N/28a92M8bnwn6zq2KiMxjJaEt+xn08kIlrp0ztfNn1giex9dTM38BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59fe726373bf87d43c0385f51a79edcec077e77c4f9ae2a37cee1d792b8fe634","last_reissued_at":"2026-07-05T11:17:00.580646Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:00.580646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Angelica Aviles-Rivero, Carola-Bibiane Sch\\\"onlieb, Mikhail Kennerley, Robby T. Tan","submitted_at":"2025-06-05T08:16:15Z","abstract_excerpt":"Combining multiple object detection datasets offers a path to improved generalisation but is hindered by inconsistencies in class semantics and bounding box annotations. Some methods to address this assume shared label taxonomies and address only spatial inconsistencies; others require manual relabelling, or produce a unified label space, which may be unsuitable when a fixed target label space is required. We propose Label-Aligned Transfer (LAT), a label transfer framework that systematically projects annotations from diverse source datasets into the label space of a target dataset. LAT begins"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04737","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/2506.04737/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":"2506.04737","created_at":"2026-07-05T11:17:00.580705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.04737v2","created_at":"2026-07-05T11:17:00.580705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04737","created_at":"2026-07-05T11:17:00.580705+00:00"},{"alias_kind":"pith_short_12","alias_value":"LH7HEY3TX6D5","created_at":"2026-07-05T11:17:00.580705+00:00"},{"alias_kind":"pith_short_16","alias_value":"LH7HEY3TX6D5IPAD","created_at":"2026-07-05T11:17:00.580705+00:00"},{"alias_kind":"pith_short_8","alias_value":"LH7HEY3T","created_at":"2026-07-05T11:17:00.580705+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.11042","citing_title":"Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08230","citing_title":"Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges","ref_index":74,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3","json":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3.json","graph_json":"https://pith.science/api/pith-number/LH7HEY3TX6D5IPADQX2RU6PNZ3/graph.json","events_json":"https://pith.science/api/pith-number/LH7HEY3TX6D5IPADQX2RU6PNZ3/events.json","paper":"https://pith.science/paper/LH7HEY3T"},"agent_actions":{"view_html":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3","download_json":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3.json","view_paper":"https://pith.science/paper/LH7HEY3T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.04737&json=true","fetch_graph":"https://pith.science/api/pith-number/LH7HEY3TX6D5IPADQX2RU6PNZ3/graph.json","fetch_events":"https://pith.science/api/pith-number/LH7HEY3TX6D5IPADQX2RU6PNZ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3/action/storage_attestation","attest_author":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3/action/author_attestation","sign_citation":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3/action/citation_signature","submit_replication":"https://pith.science/pith/LH7HEY3TX6D5IPADQX2RU6PNZ3/action/replication_record"}},"created_at":"2026-07-05T11:17:00.580705+00:00","updated_at":"2026-07-05T11:17:00.580705+00:00"}