{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:R6IJBS675GHMNHEWQI5R7ERH2O","short_pith_number":"pith:R6IJBS67","schema_version":"1.0","canonical_sha256":"8f9090cbdfe98ec69c96823b1f9227d3a30bf489566bba93199603637cd66b4e","source":{"kind":"arxiv","id":"2203.02688","version":1},"attestation_state":"computed","paper":{"title":"Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Huchuan Lu, Lihe Zhang, Tian-Zhu Xiang, Xiaoqi Zhao, Youwei Pang","submitted_at":"2022-03-05T09:13:52Z","abstract_excerpt":"The recently proposed camouflaged object detection (COD) attempts to segment objects that are visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from high intrinsic similarity between the camouflaged objects and their background, the objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To deal with these problems, we propose a mixed-scale triplet network, \\textbf{ZoomNet}, which mimics the behavior of humans when observing vague images, i.e., zooming in and out. Specifically, our ZoomNet employs th"},"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":"2203.02688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-05T09:13:52Z","cross_cats_sorted":[],"title_canon_sha256":"4a1ca1d7bcc998d386a4d0d26d1b9965cd6542db2a114afdbafb6b3b5b27c542","abstract_canon_sha256":"596968a2279cb23c2d48d4174c7b3c7091599616ca6bbc28d53d9bdf94c8e1df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:07:03.647386Z","signature_b64":"NBSay3eeWiyttdju2jc0VqgLKkMWM/sKwPbAPui5prWwanAXJSmP3OVhymvUOUp9oLmHqFAmImGYRzryH7NXAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f9090cbdfe98ec69c96823b1f9227d3a30bf489566bba93199603637cd66b4e","last_reissued_at":"2026-07-05T07:07:03.646872Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:07:03.646872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Huchuan Lu, Lihe Zhang, Tian-Zhu Xiang, Xiaoqi Zhao, Youwei Pang","submitted_at":"2022-03-05T09:13:52Z","abstract_excerpt":"The recently proposed camouflaged object detection (COD) attempts to segment objects that are visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from high intrinsic similarity between the camouflaged objects and their background, the objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To deal with these problems, we propose a mixed-scale triplet network, \\textbf{ZoomNet}, which mimics the behavior of humans when observing vague images, i.e., zooming in and out. Specifically, our ZoomNet employs th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.02688","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/2203.02688/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":"2203.02688","created_at":"2026-07-05T07:07:03.646925+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.02688v1","created_at":"2026-07-05T07:07:03.646925+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.02688","created_at":"2026-07-05T07:07:03.646925+00:00"},{"alias_kind":"pith_short_12","alias_value":"R6IJBS675GHM","created_at":"2026-07-05T07:07:03.646925+00:00"},{"alias_kind":"pith_short_16","alias_value":"R6IJBS675GHMNHEW","created_at":"2026-07-05T07:07:03.646925+00:00"},{"alias_kind":"pith_short_8","alias_value":"R6IJBS67","created_at":"2026-07-05T07:07:03.646925+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02603","citing_title":"COD10K-C: Benchmarking Robustness of Camouflaged Object Detection Under Natural Image Corruptions","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O","json":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O.json","graph_json":"https://pith.science/api/pith-number/R6IJBS675GHMNHEWQI5R7ERH2O/graph.json","events_json":"https://pith.science/api/pith-number/R6IJBS675GHMNHEWQI5R7ERH2O/events.json","paper":"https://pith.science/paper/R6IJBS67"},"agent_actions":{"view_html":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O","download_json":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O.json","view_paper":"https://pith.science/paper/R6IJBS67","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.02688&json=true","fetch_graph":"https://pith.science/api/pith-number/R6IJBS675GHMNHEWQI5R7ERH2O/graph.json","fetch_events":"https://pith.science/api/pith-number/R6IJBS675GHMNHEWQI5R7ERH2O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O/action/storage_attestation","attest_author":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O/action/author_attestation","sign_citation":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O/action/citation_signature","submit_replication":"https://pith.science/pith/R6IJBS675GHMNHEWQI5R7ERH2O/action/replication_record"}},"created_at":"2026-07-05T07:07:03.646925+00:00","updated_at":"2026-07-05T07:07:03.646925+00:00"}