{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:RODUP6WHYZDCZ5XGLYMT4ZRO3L","short_pith_number":"pith:RODUP6WH","schema_version":"1.0","canonical_sha256":"8b8747fac7c6462cf6e65e193e662edadea7de167c68a09a223975111a182767","source":{"kind":"arxiv","id":"2205.12853","version":2},"attestation_state":"computed","paper":{"title":"Deep Gradient Learning for Efficient Camouflaged Object Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Liniger, Deng-Ping Fan, Dengxin Dai, Ge-Peng Ji, Luc Van Gool, Yu-Cheng Chou","submitted_at":"2022-05-25T15:25:18Z","abstract_excerpt":"This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge mo"},"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":"2205.12853","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-25T15:25:18Z","cross_cats_sorted":[],"title_canon_sha256":"e67f97cc6d703737e57bbca83d3b64bc6fdbeb638eec34a66c90f756ee0271d4","abstract_canon_sha256":"8497da12f49bbc8ed1039cdb5ae0df5f783796521a59c27b8b34a03b6c7a769b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:17:25.626026Z","signature_b64":"54z846X0/9tsn96zmoW6/ZZsKW5zMYX9WeE2D2uL29eXz+SFCZ9BIGvQdq7cwLZ9QLeImSz0T70UcKVmf/qpDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b8747fac7c6462cf6e65e193e662edadea7de167c68a09a223975111a182767","last_reissued_at":"2026-07-05T06:17:25.625503Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:17:25.625503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Gradient Learning for Efficient Camouflaged Object Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Liniger, Deng-Ping Fan, Dengxin Dai, Ge-Peng Ji, Luc Van Gool, Yu-Cheng Chou","submitted_at":"2022-05-25T15:25:18Z","abstract_excerpt":"This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.12853","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/2205.12853/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":"2205.12853","created_at":"2026-07-05T06:17:25.625563+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.12853v2","created_at":"2026-07-05T06:17:25.625563+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.12853","created_at":"2026-07-05T06:17:25.625563+00:00"},{"alias_kind":"pith_short_12","alias_value":"RODUP6WHYZDC","created_at":"2026-07-05T06:17:25.625563+00:00"},{"alias_kind":"pith_short_16","alias_value":"RODUP6WHYZDCZ5XG","created_at":"2026-07-05T06:17:25.625563+00:00"},{"alias_kind":"pith_short_8","alias_value":"RODUP6WH","created_at":"2026-07-05T06:17:25.625563+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/RODUP6WHYZDCZ5XGLYMT4ZRO3L","json":"https://pith.science/pith/RODUP6WHYZDCZ5XGLYMT4ZRO3L.json","graph_json":"https://pith.science/api/pith-number/RODUP6WHYZDCZ5XGLYMT4ZRO3L/graph.json","events_json":"https://pith.science/api/pith-number/RODUP6WHYZDCZ5XGLYMT4ZRO3L/events.json","paper":"https://pith.science/paper/RODUP6WH"},"agent_actions":{"view_html":"https://pith.science/pith/RODUP6WHYZDCZ5XGLYMT4ZRO3L","download_json":"https://pith.science/pith/RODUP6WHYZDCZ5XGLYMT4ZRO3L.json","view_paper":"https://pith.science/paper/RODUP6WH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.12853&json=true","fetch_graph":"https://pith.science/api/pith-number/RODUP6WHYZDCZ5XGLYMT4ZRO3L/graph.json","fetch_events":"https://pith.science/api/pith-number/RODUP6WHYZDCZ5XGLYMT4ZRO3L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RODUP6WHYZDCZ5XGLYMT4ZRO3L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RODUP6WHYZDCZ5XGLYMT4ZRO3L/action/storage_attestation","attest_author":"https://pith.science/pith/RODUP6WHYZDCZ5XGLYMT4ZRO3L/action/author_attestation","sign_citation":"https://pith.science/pith/RODUP6WHYZDCZ5XGLYMT4ZRO3L/action/citation_signature","submit_replication":"https://pith.science/pith/RODUP6WHYZDCZ5XGLYMT4ZRO3L/action/replication_record"}},"created_at":"2026-07-05T06:17:25.625563+00:00","updated_at":"2026-07-05T06:17:25.625563+00:00"}