{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:33JRJ55GV23XN2I2WTPQH67TVE","short_pith_number":"pith:33JRJ55G","canonical_record":{"source":{"id":"1802.07957","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-22T09:55:29Z","cross_cats_sorted":[],"title_canon_sha256":"c430d4f78731b298c6d4e6e4313ea970e347f5a6249f22089ef93e6e52d3c5aa","abstract_canon_sha256":"34edbb2454a6ea09e5040dbd2448e544534ce00e06d82acca025a0854e6eeeec"},"schema_version":"1.0"},"canonical_sha256":"ded314f7a6aeb776e91ab4df03fbf3a917d931e21d5232c73745de81760f5074","source":{"kind":"arxiv","id":"1802.07957","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.07957","created_at":"2026-05-17T23:52:27Z"},{"alias_kind":"arxiv_version","alias_value":"1802.07957v2","created_at":"2026-05-17T23:52:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.07957","created_at":"2026-05-17T23:52:27Z"},{"alias_kind":"pith_short_12","alias_value":"33JRJ55GV23X","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"33JRJ55GV23XN2I2","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"33JRJ55G","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:33JRJ55GV23XN2I2WTPQH67TVE","target":"record","payload":{"canonical_record":{"source":{"id":"1802.07957","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-22T09:55:29Z","cross_cats_sorted":[],"title_canon_sha256":"c430d4f78731b298c6d4e6e4313ea970e347f5a6249f22089ef93e6e52d3c5aa","abstract_canon_sha256":"34edbb2454a6ea09e5040dbd2448e544534ce00e06d82acca025a0854e6eeeec"},"schema_version":"1.0"},"canonical_sha256":"ded314f7a6aeb776e91ab4df03fbf3a917d931e21d5232c73745de81760f5074","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:27.509293Z","signature_b64":"AxUKO6oRWcIHrl7yRlAzxjhm1Fv7p9prUrV+Z6zJnFU4XCJP6YZtkLAEiZmydOVffG2jp0fAk25swVMtkTliCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ded314f7a6aeb776e91ab4df03fbf3a917d931e21d5232c73745de81760f5074","last_reissued_at":"2026-05-17T23:52:27.508654Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:27.508654Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.07957","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":"2026-05-17T23:52:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ozpm52wtV/PkE0AbHsn2p8X3OTsEqdUIsTLrQoj9M9YadvC4Kz4GruBZ4QRks6xWDcUr7j6AyR9M7xhlV3pJAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T04:29:19.719911Z"},"content_sha256":"3ecf6aadfcb8fa7bfbf52afed07635adfd51741105d8b5c36f2548e2433f35de","schema_version":"1.0","event_id":"sha256:3ecf6aadfcb8fa7bfbf52afed07635adfd51741105d8b5c36f2548e2433f35de"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:33JRJ55GV23XN2I2WTPQH67TVE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunhua Shen, Dong Wang, Hongyu Wang, Huchuan Lu, Pingping Zhang, Wei Liu, Yinjie Lei","submitted_at":"2018-02-22T09:55:29Z","abstract_excerpt":"In this paper, we propose a novel effective non-rigid object tracking framework based on the spatial-temporal consistent saliency detection. In contrast to most existing trackers that utilize a bounding box to specify the tracked target, the proposed framework can extract accurate regions of the target as tracking outputs. It achieves a better description of the non-rigid objects and reduces the background pollution for the tracking model. Furthermore, our model has several unique features. First, a tailored fully convolutional neural network (TFCN) is developed to model the local saliency pri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.07957","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":"2026-05-17T23:52:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uiL4SV7IuQJTfm7BqC7k29qOtPSqnewDRZRxKXLOan3lE5alawvYRpIvdq/ojQZwGNSlWuqP02nCKAWxX7MRBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T04:29:19.720481Z"},"content_sha256":"0c4c06322fff53ce0ca2698e8d241f112a0554a054f476407e1e6aa26aa61ed4","schema_version":"1.0","event_id":"sha256:0c4c06322fff53ce0ca2698e8d241f112a0554a054f476407e1e6aa26aa61ed4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/33JRJ55GV23XN2I2WTPQH67TVE/bundle.json","state_url":"https://pith.science/pith/33JRJ55GV23XN2I2WTPQH67TVE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/33JRJ55GV23XN2I2WTPQH67TVE/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":"2026-05-26T04:29:19Z","links":{"resolver":"https://pith.science/pith/33JRJ55GV23XN2I2WTPQH67TVE","bundle":"https://pith.science/pith/33JRJ55GV23XN2I2WTPQH67TVE/bundle.json","state":"https://pith.science/pith/33JRJ55GV23XN2I2WTPQH67TVE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/33JRJ55GV23XN2I2WTPQH67TVE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:33JRJ55GV23XN2I2WTPQH67TVE","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":"34edbb2454a6ea09e5040dbd2448e544534ce00e06d82acca025a0854e6eeeec","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-22T09:55:29Z","title_canon_sha256":"c430d4f78731b298c6d4e6e4313ea970e347f5a6249f22089ef93e6e52d3c5aa"},"schema_version":"1.0","source":{"id":"1802.07957","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.07957","created_at":"2026-05-17T23:52:27Z"},{"alias_kind":"arxiv_version","alias_value":"1802.07957v2","created_at":"2026-05-17T23:52:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.07957","created_at":"2026-05-17T23:52:27Z"},{"alias_kind":"pith_short_12","alias_value":"33JRJ55GV23X","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"33JRJ55GV23XN2I2","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"33JRJ55G","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:0c4c06322fff53ce0ca2698e8d241f112a0554a054f476407e1e6aa26aa61ed4","target":"graph","created_at":"2026-05-17T23:52:27Z","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":"In this paper, we propose a novel effective non-rigid object tracking framework based on the spatial-temporal consistent saliency detection. In contrast to most existing trackers that utilize a bounding box to specify the tracked target, the proposed framework can extract accurate regions of the target as tracking outputs. It achieves a better description of the non-rigid objects and reduces the background pollution for the tracking model. Furthermore, our model has several unique features. First, a tailored fully convolutional neural network (TFCN) is developed to model the local saliency pri","authors_text":"Chunhua Shen, Dong Wang, Hongyu Wang, Huchuan Lu, Pingping Zhang, Wei Liu, Yinjie Lei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-22T09:55:29Z","title":"Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.07957","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":"sha256:3ecf6aadfcb8fa7bfbf52afed07635adfd51741105d8b5c36f2548e2433f35de","target":"record","created_at":"2026-05-17T23:52:27Z","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":"34edbb2454a6ea09e5040dbd2448e544534ce00e06d82acca025a0854e6eeeec","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-22T09:55:29Z","title_canon_sha256":"c430d4f78731b298c6d4e6e4313ea970e347f5a6249f22089ef93e6e52d3c5aa"},"schema_version":"1.0","source":{"id":"1802.07957","kind":"arxiv","version":2}},"canonical_sha256":"ded314f7a6aeb776e91ab4df03fbf3a917d931e21d5232c73745de81760f5074","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ded314f7a6aeb776e91ab4df03fbf3a917d931e21d5232c73745de81760f5074","first_computed_at":"2026-05-17T23:52:27.508654Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:27.508654Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AxUKO6oRWcIHrl7yRlAzxjhm1Fv7p9prUrV+Z6zJnFU4XCJP6YZtkLAEiZmydOVffG2jp0fAk25swVMtkTliCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:27.509293Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.07957","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ecf6aadfcb8fa7bfbf52afed07635adfd51741105d8b5c36f2548e2433f35de","sha256:0c4c06322fff53ce0ca2698e8d241f112a0554a054f476407e1e6aa26aa61ed4"],"state_sha256":"3e18619feb0ed91f31663d0cc8e0622bb5722dbbecbd73c6f0b5177d90ed6f7b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F6/uI0h+BehVgR1h2rTVU0xpsAMq4kG6MEjnlQAsr1eUp/VtnHfgasSELBxhKq4hJWEguiBaC2oGEF+mHxb+CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T04:29:19.724067Z","bundle_sha256":"806b87892428770761f1d5310cc55b77b6800e53598f165114287a9b680bb86e"}}