{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MS2BSM7WXJT4AOVJMZN5SOLA44","short_pith_number":"pith:MS2BSM7W","schema_version":"1.0","canonical_sha256":"64b41933f6ba67c03aa9665bd93960e7230b468f75a1df4974a28a3a1e28222e","source":{"kind":"arxiv","id":"2605.25385","version":1},"attestation_state":"computed","paper":{"title":"Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Junyu Dong, Lin Qi, Xia Li, Xinran Liu","submitted_at":"2026-05-25T03:26:13Z","abstract_excerpt":"Camouflaged object detection (COD) from a single image is a challenging task due to the high similarity between objects and their surroundings. Existing fully supervised methods require labor-intensive pixel-level annotations, making weakly supervised methods a viable compromise that balances accuracy and annotation efficiency. However, weakly supervised methods often experience performance degradation due to the use of coarse annotations. In this paper, we introduce a new weakly supervised approach for camouflaged object detection to overcome these limitations. Specifically, we propose a nove"},"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":"2605.25385","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-25T03:26:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8314cca51e818ae71c96f9d49ebc4349cffde38f8d7d45b8b43f932fbaef615d","abstract_canon_sha256":"fe6d33af0733a205adfef2217c1d8cb92d223e74da3326e7763e825516c39341"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:04:31.994656Z","signature_b64":"/WjqiiiSNEmDb9FKx46kWR4DN6WVsoDUSu7dVJLcEhxbbTS4p74yiuHYS1YvwbRoHXUIUeNwybL3BQEqIYT2Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64b41933f6ba67c03aa9665bd93960e7230b468f75a1df4974a28a3a1e28222e","last_reissued_at":"2026-05-26T02:04:31.993866Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:04:31.993866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Junyu Dong, Lin Qi, Xia Li, Xinran Liu","submitted_at":"2026-05-25T03:26:13Z","abstract_excerpt":"Camouflaged object detection (COD) from a single image is a challenging task due to the high similarity between objects and their surroundings. Existing fully supervised methods require labor-intensive pixel-level annotations, making weakly supervised methods a viable compromise that balances accuracy and annotation efficiency. However, weakly supervised methods often experience performance degradation due to the use of coarse annotations. In this paper, we introduce a new weakly supervised approach for camouflaged object detection to overcome these limitations. Specifically, we propose a nove"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.25385","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/2605.25385/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":"2605.25385","created_at":"2026-05-26T02:04:31.993996+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.25385v1","created_at":"2026-05-26T02:04:31.993996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.25385","created_at":"2026-05-26T02:04:31.993996+00:00"},{"alias_kind":"pith_short_12","alias_value":"MS2BSM7WXJT4","created_at":"2026-05-26T02:04:31.993996+00:00"},{"alias_kind":"pith_short_16","alias_value":"MS2BSM7WXJT4AOVJ","created_at":"2026-05-26T02:04:31.993996+00:00"},{"alias_kind":"pith_short_8","alias_value":"MS2BSM7W","created_at":"2026-05-26T02:04:31.993996+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/MS2BSM7WXJT4AOVJMZN5SOLA44","json":"https://pith.science/pith/MS2BSM7WXJT4AOVJMZN5SOLA44.json","graph_json":"https://pith.science/api/pith-number/MS2BSM7WXJT4AOVJMZN5SOLA44/graph.json","events_json":"https://pith.science/api/pith-number/MS2BSM7WXJT4AOVJMZN5SOLA44/events.json","paper":"https://pith.science/paper/MS2BSM7W"},"agent_actions":{"view_html":"https://pith.science/pith/MS2BSM7WXJT4AOVJMZN5SOLA44","download_json":"https://pith.science/pith/MS2BSM7WXJT4AOVJMZN5SOLA44.json","view_paper":"https://pith.science/paper/MS2BSM7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.25385&json=true","fetch_graph":"https://pith.science/api/pith-number/MS2BSM7WXJT4AOVJMZN5SOLA44/graph.json","fetch_events":"https://pith.science/api/pith-number/MS2BSM7WXJT4AOVJMZN5SOLA44/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MS2BSM7WXJT4AOVJMZN5SOLA44/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MS2BSM7WXJT4AOVJMZN5SOLA44/action/storage_attestation","attest_author":"https://pith.science/pith/MS2BSM7WXJT4AOVJMZN5SOLA44/action/author_attestation","sign_citation":"https://pith.science/pith/MS2BSM7WXJT4AOVJMZN5SOLA44/action/citation_signature","submit_replication":"https://pith.science/pith/MS2BSM7WXJT4AOVJMZN5SOLA44/action/replication_record"}},"created_at":"2026-05-26T02:04:31.993996+00:00","updated_at":"2026-05-26T02:04:31.993996+00:00"}