{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6RNVKHD3A24M5ZD3W7GTRR3GYE","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":"287db11d085a02d44ad4784c983b6c23da9670394d003b516986f902c314fcfe","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-12T14:54:36Z","title_canon_sha256":"b0b65f80db019ac5b03d255856c67fda497680c9d4773988b549a56051c31f41"},"schema_version":"1.0","source":{"id":"1801.04187","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.04187","created_at":"2026-05-18T00:26:09Z"},{"alias_kind":"arxiv_version","alias_value":"1801.04187v1","created_at":"2026-05-18T00:26:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.04187","created_at":"2026-05-18T00:26:09Z"},{"alias_kind":"pith_short_12","alias_value":"6RNVKHD3A24M","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6RNVKHD3A24M5ZD3","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6RNVKHD3","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:5c7fc70b3efc85772220da2c160cc45520f3d71218a2bb8ad07ab557012f4e96","target":"graph","created_at":"2026-05-18T00:26:09Z","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":"Salient object detection is a fundamental problem and has been received a great deal of attentions in computer vision. Recently deep learning model became a powerful tool for image feature extraction. In this paper, we propose a multi-scale deep neural network (MSDNN) for salient object detection. The proposed model first extracts global high-level features and context information over the whole source image with recurrent convolutional neural network (RCNN). Then several stacked deconvolutional layers are adopted to get the multi-scale feature representation and obtain a series of saliency ma","authors_text":"Chunhong Cao, Fen Xiao, Kai Hu, Liangchan Peng, Wenzheng Deng, Xieping Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-12T14:54:36Z","title":"MSDNN: Multi-Scale Deep Neural Network for Salient Object Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.04187","kind":"arxiv","version":1},"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:bad7a00672386b5062c15aa269c0934846e14630093727b7d9e04fd23dfff699","target":"record","created_at":"2026-05-18T00:26:09Z","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":"287db11d085a02d44ad4784c983b6c23da9670394d003b516986f902c314fcfe","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-12T14:54:36Z","title_canon_sha256":"b0b65f80db019ac5b03d255856c67fda497680c9d4773988b549a56051c31f41"},"schema_version":"1.0","source":{"id":"1801.04187","kind":"arxiv","version":1}},"canonical_sha256":"f45b551c7b06b8cee47bb7cd38c766c10cac2b19c0903d504175a82bda1b89a6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f45b551c7b06b8cee47bb7cd38c766c10cac2b19c0903d504175a82bda1b89a6","first_computed_at":"2026-05-18T00:26:09.555767Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:26:09.555767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"URG5k+kEneIpbKP6wCgoJbGZykcOqjSQjBiqibZCA7/sIV88x3t/99BVdLVNuRfUHJ0uRf3EXV96doqFz9EZDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:26:09.556343Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.04187","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bad7a00672386b5062c15aa269c0934846e14630093727b7d9e04fd23dfff699","sha256:5c7fc70b3efc85772220da2c160cc45520f3d71218a2bb8ad07ab557012f4e96"],"state_sha256":"759365b31c27c1b8039ca54be700717657551494f3b7c6a30f545508f77c4584"}