{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BSEF677O35VGFWUNMUX7SOJEP4","short_pith_number":"pith:BSEF677O","schema_version":"1.0","canonical_sha256":"0c885f7feedf6a62da8d652ff939247f3d63b33313c2d4c150d02b91708c0aeb","source":{"kind":"arxiv","id":"2506.02882","version":2},"attestation_state":"computed","paper":{"title":"GaRA-SAM: Robustifying Segment Anything Model with Gated-Rank Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lukas Hoyer, Sohyun Lee, Suha Kwak, Yeho Gwon","submitted_at":"2025-06-03T13:47:59Z","abstract_excerpt":"Improving robustness of the Segment Anything Model (SAM) to input degradations is critical for its deployment in high-stakes applications such as autonomous driving and robotics. Our approach to this challenge prioritizes three key aspects: first, parameter efficiency to maintain the inherent generalization capability of SAM; second, fine-grained and input-aware robustification to precisely address the input corruption; and third, adherence to standard training protocols for ease of training. To this end, we propose gated-rank adaptation (GaRA). GaRA introduces lightweight adapters into interm"},"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":"2506.02882","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-03T13:47:59Z","cross_cats_sorted":[],"title_canon_sha256":"88d92c729e20403e9867ea2fd9a6f0ee6c70fe6f2f74cf116a94190a6e221f23","abstract_canon_sha256":"a1a888f6aa13f40d0075f1e40bcf310d63ad4e2ec7b88d19e24bc18a3d6f14d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:09.731638Z","signature_b64":"Zefw0jClVugpPedBbgWHf+aXO2N8OTe2CdO2hABU3/v7QsuLGU2FBYwUCC7wf30idS0VbqmibkEZRJMJBqp/AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c885f7feedf6a62da8d652ff939247f3d63b33313c2d4c150d02b91708c0aeb","last_reissued_at":"2026-07-05T11:16:09.731037Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:09.731037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GaRA-SAM: Robustifying Segment Anything Model with Gated-Rank Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lukas Hoyer, Sohyun Lee, Suha Kwak, Yeho Gwon","submitted_at":"2025-06-03T13:47:59Z","abstract_excerpt":"Improving robustness of the Segment Anything Model (SAM) to input degradations is critical for its deployment in high-stakes applications such as autonomous driving and robotics. Our approach to this challenge prioritizes three key aspects: first, parameter efficiency to maintain the inherent generalization capability of SAM; second, fine-grained and input-aware robustification to precisely address the input corruption; and third, adherence to standard training protocols for ease of training. To this end, we propose gated-rank adaptation (GaRA). GaRA introduces lightweight adapters into interm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02882","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/2506.02882/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":"2506.02882","created_at":"2026-07-05T11:16:09.731099+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.02882v2","created_at":"2026-07-05T11:16:09.731099+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02882","created_at":"2026-07-05T11:16:09.731099+00:00"},{"alias_kind":"pith_short_12","alias_value":"BSEF677O35VG","created_at":"2026-07-05T11:16:09.731099+00:00"},{"alias_kind":"pith_short_16","alias_value":"BSEF677O35VGFWUN","created_at":"2026-07-05T11:16:09.731099+00:00"},{"alias_kind":"pith_short_8","alias_value":"BSEF677O","created_at":"2026-07-05T11:16:09.731099+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/BSEF677O35VGFWUNMUX7SOJEP4","json":"https://pith.science/pith/BSEF677O35VGFWUNMUX7SOJEP4.json","graph_json":"https://pith.science/api/pith-number/BSEF677O35VGFWUNMUX7SOJEP4/graph.json","events_json":"https://pith.science/api/pith-number/BSEF677O35VGFWUNMUX7SOJEP4/events.json","paper":"https://pith.science/paper/BSEF677O"},"agent_actions":{"view_html":"https://pith.science/pith/BSEF677O35VGFWUNMUX7SOJEP4","download_json":"https://pith.science/pith/BSEF677O35VGFWUNMUX7SOJEP4.json","view_paper":"https://pith.science/paper/BSEF677O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.02882&json=true","fetch_graph":"https://pith.science/api/pith-number/BSEF677O35VGFWUNMUX7SOJEP4/graph.json","fetch_events":"https://pith.science/api/pith-number/BSEF677O35VGFWUNMUX7SOJEP4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BSEF677O35VGFWUNMUX7SOJEP4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BSEF677O35VGFWUNMUX7SOJEP4/action/storage_attestation","attest_author":"https://pith.science/pith/BSEF677O35VGFWUNMUX7SOJEP4/action/author_attestation","sign_citation":"https://pith.science/pith/BSEF677O35VGFWUNMUX7SOJEP4/action/citation_signature","submit_replication":"https://pith.science/pith/BSEF677O35VGFWUNMUX7SOJEP4/action/replication_record"}},"created_at":"2026-07-05T11:16:09.731099+00:00","updated_at":"2026-07-05T11:16:09.731099+00:00"}