{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:XGXWCPLIHEOKOZL73F5CXZUWRH","short_pith_number":"pith:XGXWCPLI","schema_version":"1.0","canonical_sha256":"b9af613d68391ca7657fd97a2be69689ec92e39e15426e2b4d6858a8ca2dc4f8","source":{"kind":"arxiv","id":"2011.06252","version":2},"attestation_state":"computed","paper":{"title":"SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Junaed Sattar, Md Jahidul Islam, Ruobing Wang","submitted_at":"2020-11-12T08:17:21Z","abstract_excerpt":"This paper presents a holistic approach to saliency-guided visual attention modeling (SVAM) for use by autonomous underwater robots. Our proposed model, named SVAM-Net, integrates deep visual features at various scales and semantics for effective salient object detection (SOD) in natural underwater images. The SVAM-Net architecture is configured in a unique way to jointly accommodate bottom-up and top-down learning within two separate branches of the network while sharing the same encoding layers. We design dedicated spatial attention modules (SAMs) along these learning pathways to exploit the"},"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":"2011.06252","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-12T08:17:21Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"078a4e39fffa87909194aa537bcf55699f2c61c0a47a2d0a76ecbbbc472ff425","abstract_canon_sha256":"343ede53ab4cdaccb9521d28067b90fcd1dce7259d2b3c5d089672f9f23e0694"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:14:36.235833Z","signature_b64":"+aLhCe1kOtKdNZ8IAqVgbjUpEc3svYTLyiGlILIHUEM884TL4qi4qDne9GQ4zEO09bi4UqZvqOEhWwMDEzRsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9af613d68391ca7657fd97a2be69689ec92e39e15426e2b4d6858a8ca2dc4f8","last_reissued_at":"2026-07-05T04:14:36.235274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:14:36.235274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Junaed Sattar, Md Jahidul Islam, Ruobing Wang","submitted_at":"2020-11-12T08:17:21Z","abstract_excerpt":"This paper presents a holistic approach to saliency-guided visual attention modeling (SVAM) for use by autonomous underwater robots. Our proposed model, named SVAM-Net, integrates deep visual features at various scales and semantics for effective salient object detection (SOD) in natural underwater images. The SVAM-Net architecture is configured in a unique way to jointly accommodate bottom-up and top-down learning within two separate branches of the network while sharing the same encoding layers. We design dedicated spatial attention modules (SAMs) along these learning pathways to exploit the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.06252","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/2011.06252/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":"2011.06252","created_at":"2026-07-05T04:14:36.235339+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.06252v2","created_at":"2026-07-05T04:14:36.235339+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.06252","created_at":"2026-07-05T04:14:36.235339+00:00"},{"alias_kind":"pith_short_12","alias_value":"XGXWCPLIHEOK","created_at":"2026-07-05T04:14:36.235339+00:00"},{"alias_kind":"pith_short_16","alias_value":"XGXWCPLIHEOKOZL7","created_at":"2026-07-05T04:14:36.235339+00:00"},{"alias_kind":"pith_short_8","alias_value":"XGXWCPLI","created_at":"2026-07-05T04:14:36.235339+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07146","citing_title":"UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH","json":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH.json","graph_json":"https://pith.science/api/pith-number/XGXWCPLIHEOKOZL73F5CXZUWRH/graph.json","events_json":"https://pith.science/api/pith-number/XGXWCPLIHEOKOZL73F5CXZUWRH/events.json","paper":"https://pith.science/paper/XGXWCPLI"},"agent_actions":{"view_html":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH","download_json":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH.json","view_paper":"https://pith.science/paper/XGXWCPLI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.06252&json=true","fetch_graph":"https://pith.science/api/pith-number/XGXWCPLIHEOKOZL73F5CXZUWRH/graph.json","fetch_events":"https://pith.science/api/pith-number/XGXWCPLIHEOKOZL73F5CXZUWRH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH/action/storage_attestation","attest_author":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH/action/author_attestation","sign_citation":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH/action/citation_signature","submit_replication":"https://pith.science/pith/XGXWCPLIHEOKOZL73F5CXZUWRH/action/replication_record"}},"created_at":"2026-07-05T04:14:36.235339+00:00","updated_at":"2026-07-05T04:14:36.235339+00:00"}