{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BP6QRTL4ASDYU6KGCTFPWGQUME","short_pith_number":"pith:BP6QRTL4","schema_version":"1.0","canonical_sha256":"0bfd08cd7c04878a794614cafb1a146139a2f5b6c8561ea7dee2900e23590598","source":{"kind":"arxiv","id":"2606.20130","version":1},"attestation_state":"computed","paper":{"title":"SAM3 Self-Distillation for Fine-Grained GOOSE 2D Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Xuesong Wang","submitted_at":"2026-06-18T11:54:07Z","abstract_excerpt":"We describe our 4th-place entry to the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, which reached a composite mean Intersection-over-Union (mIoU) of 69.73% on the official 1,815-image test set. Our model adapts the image encoder of a recent visual foundation model, Segment Anything Model 3 (SAM3), with a lightweight decoder. Beyond this, we contribute two techniques and one empirical finding: (i) a self-distillation scheme that re-uses SAM3 itself, prompted with ground-truth boxes, as a teacher on the classes where it outperforms our own model; (ii) an image-level multi-sca"},"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":"2606.20130","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-18T11:54:07Z","cross_cats_sorted":[],"title_canon_sha256":"c58a4dcfabe0b074ad49a2eabc1c99e84ab96f37154a19a640e8a9a4c9ade200","abstract_canon_sha256":"b42a3e9b70722ccaa02ea72fa3ca81b806a809fc3f29a7c8438730fd25c213a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:13:03.581101Z","signature_b64":"EBwWrK3JrUVL4eZ8O8XSJdVfizEX+LprwNXULq5qZr51j/WSgAkrv3c60HhMePLlys6uDFEmNtcOLJKomNMwDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bfd08cd7c04878a794614cafb1a146139a2f5b6c8561ea7dee2900e23590598","last_reissued_at":"2026-06-19T16:13:03.580702Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:13:03.580702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SAM3 Self-Distillation for Fine-Grained GOOSE 2D Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Xuesong Wang","submitted_at":"2026-06-18T11:54:07Z","abstract_excerpt":"We describe our 4th-place entry to the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, which reached a composite mean Intersection-over-Union (mIoU) of 69.73% on the official 1,815-image test set. Our model adapts the image encoder of a recent visual foundation model, Segment Anything Model 3 (SAM3), with a lightweight decoder. Beyond this, we contribute two techniques and one empirical finding: (i) a self-distillation scheme that re-uses SAM3 itself, prompted with ground-truth boxes, as a teacher on the classes where it outperforms our own model; (ii) an image-level multi-sca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20130","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/2606.20130/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":"2606.20130","created_at":"2026-06-19T16:13:03.580768+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.20130v1","created_at":"2026-06-19T16:13:03.580768+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20130","created_at":"2026-06-19T16:13:03.580768+00:00"},{"alias_kind":"pith_short_12","alias_value":"BP6QRTL4ASDY","created_at":"2026-06-19T16:13:03.580768+00:00"},{"alias_kind":"pith_short_16","alias_value":"BP6QRTL4ASDYU6KG","created_at":"2026-06-19T16:13:03.580768+00:00"},{"alias_kind":"pith_short_8","alias_value":"BP6QRTL4","created_at":"2026-06-19T16:13:03.580768+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/BP6QRTL4ASDYU6KGCTFPWGQUME","json":"https://pith.science/pith/BP6QRTL4ASDYU6KGCTFPWGQUME.json","graph_json":"https://pith.science/api/pith-number/BP6QRTL4ASDYU6KGCTFPWGQUME/graph.json","events_json":"https://pith.science/api/pith-number/BP6QRTL4ASDYU6KGCTFPWGQUME/events.json","paper":"https://pith.science/paper/BP6QRTL4"},"agent_actions":{"view_html":"https://pith.science/pith/BP6QRTL4ASDYU6KGCTFPWGQUME","download_json":"https://pith.science/pith/BP6QRTL4ASDYU6KGCTFPWGQUME.json","view_paper":"https://pith.science/paper/BP6QRTL4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.20130&json=true","fetch_graph":"https://pith.science/api/pith-number/BP6QRTL4ASDYU6KGCTFPWGQUME/graph.json","fetch_events":"https://pith.science/api/pith-number/BP6QRTL4ASDYU6KGCTFPWGQUME/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BP6QRTL4ASDYU6KGCTFPWGQUME/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BP6QRTL4ASDYU6KGCTFPWGQUME/action/storage_attestation","attest_author":"https://pith.science/pith/BP6QRTL4ASDYU6KGCTFPWGQUME/action/author_attestation","sign_citation":"https://pith.science/pith/BP6QRTL4ASDYU6KGCTFPWGQUME/action/citation_signature","submit_replication":"https://pith.science/pith/BP6QRTL4ASDYU6KGCTFPWGQUME/action/replication_record"}},"created_at":"2026-06-19T16:13:03.580768+00:00","updated_at":"2026-06-19T16:13:03.580768+00:00"}