{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WMO6HHSNZWMPURNL6KLVNWSAMN","short_pith_number":"pith:WMO6HHSN","canonical_record":{"source":{"id":"1904.01828","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-03T08:14:11Z","cross_cats_sorted":[],"title_canon_sha256":"6aa46bd04666e05dd8e12fdbdcdb8e2740b5c06c3eaa0cff516aef248a38c139","abstract_canon_sha256":"4d800c693eb6ec57555f20205ac18bc0bdb4f5ae41e8d7a5682141e31d57e3ef"},"schema_version":"1.0"},"canonical_sha256":"b31de39e4dcd98fa45abf29756da40634fbf94262b747b38551be50e6bc2c6e0","source":{"kind":"arxiv","id":"1904.01828","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.01828","created_at":"2026-05-17T23:49:29Z"},{"alias_kind":"arxiv_version","alias_value":"1904.01828v1","created_at":"2026-05-17T23:49:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01828","created_at":"2026-05-17T23:49:29Z"},{"alias_kind":"pith_short_12","alias_value":"WMO6HHSNZWMP","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WMO6HHSNZWMPURNL","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WMO6HHSN","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WMO6HHSNZWMPURNL6KLVNWSAMN","target":"record","payload":{"canonical_record":{"source":{"id":"1904.01828","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-03T08:14:11Z","cross_cats_sorted":[],"title_canon_sha256":"6aa46bd04666e05dd8e12fdbdcdb8e2740b5c06c3eaa0cff516aef248a38c139","abstract_canon_sha256":"4d800c693eb6ec57555f20205ac18bc0bdb4f5ae41e8d7a5682141e31d57e3ef"},"schema_version":"1.0"},"canonical_sha256":"b31de39e4dcd98fa45abf29756da40634fbf94262b747b38551be50e6bc2c6e0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:29.546246Z","signature_b64":"EMG5vw/81wHt5UJZDjndQnN8mKUsqZg8b0zHQvnqp5l6X4zS3jv/H9zJvxJr//Exvz5TKv2HtJRy8nW2wN8XBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b31de39e4dcd98fa45abf29756da40634fbf94262b747b38551be50e6bc2c6e0","last_reissued_at":"2026-05-17T23:49:29.545580Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:29.545580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.01828","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C4w+Z9LRYXkuBC+QgbhNkEip133CpZl2Y3C0U7QI6Q01sU/ZbAeE/OFucS0d6lizKJscRxavt9JQ2F36RBRRCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T17:58:32.505108Z"},"content_sha256":"e998d4513742f3338109770300313930ece836837613977ac389f185a92aea91","schema_version":"1.0","event_id":"sha256:e998d4513742f3338109770300313930ece836837613977ac389f185a92aea91"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WMO6HHSNZWMPURNL6KLVNWSAMN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Deep Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Ma, Houqiang Li, Ning Wang, Wei Liu, Wengang Zhou, Yibing Song","submitted_at":"2019-04-03T08:14:11Z","abstract_excerpt":"We propose an unsupervised visual tracking method in this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN model is trained on large-scale unlabeled videos in an unsupervised manner. Our motivation is that a robust tracker should be effective in both the forward and backward predictions (i.e., the tracker can forward localize the target object in successive frames and backtrace to its initial position in the first frame). We build our framework on a Siamese correlation filter network, which is trained using unlabeled raw videos. Meanwhil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01828","kind":"arxiv","version":1},"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":"2026-05-17T23:49:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"glfc2jxYqmYoOQUeOdplB/Ho/7gh0s5+2EWjvH6SaRxFwbuQNQNA2m26ehBD2vhm5Zb36JQjFoazphVwP5ZQCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T17:58:32.505853Z"},"content_sha256":"8b6f2ff7d22977817d37277eb9f0bd1ceac2a5078f3807bde71a6f2af3abc2e2","schema_version":"1.0","event_id":"sha256:8b6f2ff7d22977817d37277eb9f0bd1ceac2a5078f3807bde71a6f2af3abc2e2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WMO6HHSNZWMPURNL6KLVNWSAMN/bundle.json","state_url":"https://pith.science/pith/WMO6HHSNZWMPURNL6KLVNWSAMN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WMO6HHSNZWMPURNL6KLVNWSAMN/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":"2026-06-08T17:58:32Z","links":{"resolver":"https://pith.science/pith/WMO6HHSNZWMPURNL6KLVNWSAMN","bundle":"https://pith.science/pith/WMO6HHSNZWMPURNL6KLVNWSAMN/bundle.json","state":"https://pith.science/pith/WMO6HHSNZWMPURNL6KLVNWSAMN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WMO6HHSNZWMPURNL6KLVNWSAMN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WMO6HHSNZWMPURNL6KLVNWSAMN","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":"4d800c693eb6ec57555f20205ac18bc0bdb4f5ae41e8d7a5682141e31d57e3ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-03T08:14:11Z","title_canon_sha256":"6aa46bd04666e05dd8e12fdbdcdb8e2740b5c06c3eaa0cff516aef248a38c139"},"schema_version":"1.0","source":{"id":"1904.01828","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.01828","created_at":"2026-05-17T23:49:29Z"},{"alias_kind":"arxiv_version","alias_value":"1904.01828v1","created_at":"2026-05-17T23:49:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01828","created_at":"2026-05-17T23:49:29Z"},{"alias_kind":"pith_short_12","alias_value":"WMO6HHSNZWMP","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WMO6HHSNZWMPURNL","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WMO6HHSN","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:8b6f2ff7d22977817d37277eb9f0bd1ceac2a5078f3807bde71a6f2af3abc2e2","target":"graph","created_at":"2026-05-17T23:49:29Z","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":"We propose an unsupervised visual tracking method in this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN model is trained on large-scale unlabeled videos in an unsupervised manner. Our motivation is that a robust tracker should be effective in both the forward and backward predictions (i.e., the tracker can forward localize the target object in successive frames and backtrace to its initial position in the first frame). We build our framework on a Siamese correlation filter network, which is trained using unlabeled raw videos. Meanwhil","authors_text":"Chao Ma, Houqiang Li, Ning Wang, Wei Liu, Wengang Zhou, Yibing Song","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-03T08:14:11Z","title":"Unsupervised Deep Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01828","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:e998d4513742f3338109770300313930ece836837613977ac389f185a92aea91","target":"record","created_at":"2026-05-17T23:49:29Z","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":"4d800c693eb6ec57555f20205ac18bc0bdb4f5ae41e8d7a5682141e31d57e3ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-03T08:14:11Z","title_canon_sha256":"6aa46bd04666e05dd8e12fdbdcdb8e2740b5c06c3eaa0cff516aef248a38c139"},"schema_version":"1.0","source":{"id":"1904.01828","kind":"arxiv","version":1}},"canonical_sha256":"b31de39e4dcd98fa45abf29756da40634fbf94262b747b38551be50e6bc2c6e0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b31de39e4dcd98fa45abf29756da40634fbf94262b747b38551be50e6bc2c6e0","first_computed_at":"2026-05-17T23:49:29.545580Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:29.545580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EMG5vw/81wHt5UJZDjndQnN8mKUsqZg8b0zHQvnqp5l6X4zS3jv/H9zJvxJr//Exvz5TKv2HtJRy8nW2wN8XBw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:29.546246Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.01828","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e998d4513742f3338109770300313930ece836837613977ac389f185a92aea91","sha256:8b6f2ff7d22977817d37277eb9f0bd1ceac2a5078f3807bde71a6f2af3abc2e2"],"state_sha256":"db340d3d7861b0995dbf7cedc1bfbdb4c4ee275fba1c4d4cdcc9ed2f5dd3d848"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZtMssZWIJdGfKErd/YrSr/FEBhTj2cmXMPlS67hgCrY/qyo6RxZx2u4kL/6e/mWkq2oxQT+xS+b5TpQ2SPerDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T17:58:32.510295Z","bundle_sha256":"445adec799c2ea70e3701f9460d959e2a09703437df3263ec1ff3178517b22e3"}}