{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XAUNEAQQGU2Z3LNTCD4D5NKH3Z","short_pith_number":"pith:XAUNEAQQ","schema_version":"1.0","canonical_sha256":"b828d2021035359dadb310f83eb547de56d4f42ffa81ae7cf47ff151e4278fce","source":{"kind":"arxiv","id":"2607.17754","version":1},"attestation_state":"computed","paper":{"title":"DA-Fusion: Deformable Attention-Based RGB-D Fusion Transformer for Unseen Object Instance Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Byoung-Tak Zhang, Hye-Jung Yoon, Juno Kim, Yesol Park","submitted_at":"2026-07-20T09:47:16Z","abstract_excerpt":"In logistics automation, precise segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments. Tasks such as bin-picking and shelf-picking require robust perception to handle occlusions, varying object shapes, and complex spatial arrangements. Traditional RGB-based methods tend to over-segment objects due to their reliance on texture, while depth-based methods often under-segment by focusing primarily on geometric features. To address these limitations, we propose DA-Fusion, a deformable attention-based RGB-D fusion Transformer designed for unseen obje"},"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":"2607.17754","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T09:47:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ca4989281d598017cb6321d1dfdd29daf8e9fdaefe0b8761bac430ab32b98c73","abstract_canon_sha256":"31cee62753a7676a976dca671146f2584a895ca1a58dbd4e10017da424092e83"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T02:21:58.469920Z","signature_b64":"q3obyeuWcISGlMRAt0QLNvgEHLiSSWlExrnkZMVwVbikZR6AgX5/fAAjuuDwU5na4ZHPynif0jdeYMnVRf9RCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b828d2021035359dadb310f83eb547de56d4f42ffa81ae7cf47ff151e4278fce","last_reissued_at":"2026-07-21T02:21:58.469072Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T02:21:58.469072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DA-Fusion: Deformable Attention-Based RGB-D Fusion Transformer for Unseen Object Instance Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Byoung-Tak Zhang, Hye-Jung Yoon, Juno Kim, Yesol Park","submitted_at":"2026-07-20T09:47:16Z","abstract_excerpt":"In logistics automation, precise segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments. Tasks such as bin-picking and shelf-picking require robust perception to handle occlusions, varying object shapes, and complex spatial arrangements. Traditional RGB-based methods tend to over-segment objects due to their reliance on texture, while depth-based methods often under-segment by focusing primarily on geometric features. To address these limitations, we propose DA-Fusion, a deformable attention-based RGB-D fusion Transformer designed for unseen obje"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17754","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/2607.17754/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":"2607.17754","created_at":"2026-07-21T02:21:58.469512+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.17754v1","created_at":"2026-07-21T02:21:58.469512+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17754","created_at":"2026-07-21T02:21:58.469512+00:00"},{"alias_kind":"pith_short_12","alias_value":"XAUNEAQQGU2Z","created_at":"2026-07-21T02:21:58.469512+00:00"},{"alias_kind":"pith_short_16","alias_value":"XAUNEAQQGU2Z3LNT","created_at":"2026-07-21T02:21:58.469512+00:00"},{"alias_kind":"pith_short_8","alias_value":"XAUNEAQQ","created_at":"2026-07-21T02:21:58.469512+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/XAUNEAQQGU2Z3LNTCD4D5NKH3Z","json":"https://pith.science/pith/XAUNEAQQGU2Z3LNTCD4D5NKH3Z.json","graph_json":"https://pith.science/api/pith-number/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/graph.json","events_json":"https://pith.science/api/pith-number/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/events.json","paper":"https://pith.science/paper/XAUNEAQQ"},"agent_actions":{"view_html":"https://pith.science/pith/XAUNEAQQGU2Z3LNTCD4D5NKH3Z","download_json":"https://pith.science/pith/XAUNEAQQGU2Z3LNTCD4D5NKH3Z.json","view_paper":"https://pith.science/paper/XAUNEAQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.17754&json=true","fetch_graph":"https://pith.science/api/pith-number/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/graph.json","fetch_events":"https://pith.science/api/pith-number/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/action/storage_attestation","attest_author":"https://pith.science/pith/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/action/author_attestation","sign_citation":"https://pith.science/pith/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/action/citation_signature","submit_replication":"https://pith.science/pith/XAUNEAQQGU2Z3LNTCD4D5NKH3Z/action/replication_record"}},"created_at":"2026-07-21T02:21:58.469512+00:00","updated_at":"2026-07-21T02:21:58.469512+00:00"}