{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:34MT3PHLH3P55Z7LII3AYUJ3IU","short_pith_number":"pith:34MT3PHL","canonical_record":{"source":{"id":"1807.09940","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T03:30:57Z","cross_cats_sorted":[],"title_canon_sha256":"08b0cff8e635ca83864d013eef00cfeb51a968c0e4aaa60039f10f66a6918df3","abstract_canon_sha256":"1c8260838040dbee70c8d401535c2fbf2777dcd763db7e27d9ee78631ccc5015"},"schema_version":"1.0"},"canonical_sha256":"df193dbceb3edfdee7eb42360c513b4537574ae19909086639a96b4c64ddad42","source":{"kind":"arxiv","id":"1807.09940","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.09940","created_at":"2026-05-17T23:48:40Z"},{"alias_kind":"arxiv_version","alias_value":"1807.09940v2","created_at":"2026-05-17T23:48:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09940","created_at":"2026-05-17T23:48:40Z"},{"alias_kind":"pith_short_12","alias_value":"34MT3PHLH3P5","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"34MT3PHLH3P55Z7L","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"34MT3PHL","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:34MT3PHLH3P55Z7LII3AYUJ3IU","target":"record","payload":{"canonical_record":{"source":{"id":"1807.09940","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T03:30:57Z","cross_cats_sorted":[],"title_canon_sha256":"08b0cff8e635ca83864d013eef00cfeb51a968c0e4aaa60039f10f66a6918df3","abstract_canon_sha256":"1c8260838040dbee70c8d401535c2fbf2777dcd763db7e27d9ee78631ccc5015"},"schema_version":"1.0"},"canonical_sha256":"df193dbceb3edfdee7eb42360c513b4537574ae19909086639a96b4c64ddad42","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:40.305045Z","signature_b64":"bwiAb6Rhg68fi3q4adiiHfJrIZTBzYwe6SYnT0ov6MJvWWEkUSgYMkHa39pBWuG4EIqFS6RIdiXngtLpGWUDAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df193dbceb3edfdee7eb42360c513b4537574ae19909086639a96b4c64ddad42","last_reissued_at":"2026-05-17T23:48:40.304568Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:40.304568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.09940","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pw6BgVZHt/rGhc7DN17sj3j1/Dn4VRsTwPd7C2XU2WmuLk2EN8m0WpY4cjxXspAH9480E8XCfk6oHmHI1OJSCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T12:08:07.045795Z"},"content_sha256":"03fc7dcf1bd99e0dfdd9a3e42bbffe35cefeae0a2ac7904888a9fec65ad597f4","schema_version":"1.0","event_id":"sha256:03fc7dcf1bd99e0dfdd9a3e42bbffe35cefeae0a2ac7904888a9fec65ad597f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:34MT3PHLH3P55Z7LII3AYUJ3IU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reverse Attention for Salient Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ben Wang, Shuhan Chen, Xiuli Tan, Xuelong Hu","submitted_at":"2018-07-26T03:30:57Z","abstract_excerpt":"Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents an accurate yet compact deep network for efficient salient object detection. More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09940","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GA2kqlWJPQf338Y4bC3S1LTchABM7zUxcFbwABEkdcYEAUfiq9FehTCCsmDHpem5TluIT42x7vpVv26AvrmPBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T12:08:07.046349Z"},"content_sha256":"44fe05d8b13e4cea764f508df3ce3e0b4d58d942c8440834fc9dcb90779359c3","schema_version":"1.0","event_id":"sha256:44fe05d8b13e4cea764f508df3ce3e0b4d58d942c8440834fc9dcb90779359c3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/34MT3PHLH3P55Z7LII3AYUJ3IU/bundle.json","state_url":"https://pith.science/pith/34MT3PHLH3P55Z7LII3AYUJ3IU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/34MT3PHLH3P55Z7LII3AYUJ3IU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T12:08:07Z","links":{"resolver":"https://pith.science/pith/34MT3PHLH3P55Z7LII3AYUJ3IU","bundle":"https://pith.science/pith/34MT3PHLH3P55Z7LII3AYUJ3IU/bundle.json","state":"https://pith.science/pith/34MT3PHLH3P55Z7LII3AYUJ3IU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/34MT3PHLH3P55Z7LII3AYUJ3IU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:34MT3PHLH3P55Z7LII3AYUJ3IU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1c8260838040dbee70c8d401535c2fbf2777dcd763db7e27d9ee78631ccc5015","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T03:30:57Z","title_canon_sha256":"08b0cff8e635ca83864d013eef00cfeb51a968c0e4aaa60039f10f66a6918df3"},"schema_version":"1.0","source":{"id":"1807.09940","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.09940","created_at":"2026-05-17T23:48:40Z"},{"alias_kind":"arxiv_version","alias_value":"1807.09940v2","created_at":"2026-05-17T23:48:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09940","created_at":"2026-05-17T23:48:40Z"},{"alias_kind":"pith_short_12","alias_value":"34MT3PHLH3P5","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"34MT3PHLH3P55Z7L","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"34MT3PHL","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:44fe05d8b13e4cea764f508df3ce3e0b4d58d942c8440834fc9dcb90779359c3","target":"graph","created_at":"2026-05-17T23:48:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents an accurate yet compact deep network for efficient salient object detection. More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with v","authors_text":"Ben Wang, Shuhan Chen, Xiuli Tan, Xuelong Hu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T03:30:57Z","title":"Reverse Attention for Salient Object Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09940","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:03fc7dcf1bd99e0dfdd9a3e42bbffe35cefeae0a2ac7904888a9fec65ad597f4","target":"record","created_at":"2026-05-17T23:48:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1c8260838040dbee70c8d401535c2fbf2777dcd763db7e27d9ee78631ccc5015","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T03:30:57Z","title_canon_sha256":"08b0cff8e635ca83864d013eef00cfeb51a968c0e4aaa60039f10f66a6918df3"},"schema_version":"1.0","source":{"id":"1807.09940","kind":"arxiv","version":2}},"canonical_sha256":"df193dbceb3edfdee7eb42360c513b4537574ae19909086639a96b4c64ddad42","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df193dbceb3edfdee7eb42360c513b4537574ae19909086639a96b4c64ddad42","first_computed_at":"2026-05-17T23:48:40.304568Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:40.304568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bwiAb6Rhg68fi3q4adiiHfJrIZTBzYwe6SYnT0ov6MJvWWEkUSgYMkHa39pBWuG4EIqFS6RIdiXngtLpGWUDAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:40.305045Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.09940","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:03fc7dcf1bd99e0dfdd9a3e42bbffe35cefeae0a2ac7904888a9fec65ad597f4","sha256:44fe05d8b13e4cea764f508df3ce3e0b4d58d942c8440834fc9dcb90779359c3"],"state_sha256":"9afd2468be38641a7358d127d2ce2b9868122755afe87a05fc437b5bbec2db0d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qW5XmGO8Sv7lDfsbQkQIgBdX6In1GowcOuZrPIek7Igtc6vPAlWD9THsrZt4KQ+J36eFRPDTPEk874Z+i8ZbCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T12:08:07.049520Z","bundle_sha256":"69d418ea56055f402679c4ab08e54a84776e300150a30028f13dbf55fcb9a68f"}}