{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:YN3FBX32WIPJ6P4PLYX26VN6U7","short_pith_number":"pith:YN3FBX32","canonical_record":{"source":{"id":"1806.09594","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-25T17:44:40Z","cross_cats_sorted":["cs.GR","cs.LG","cs.MM","cs.RO"],"title_canon_sha256":"91019568e4700a1ed0c4a081bf99d964258b125750cce4b45827770fbc2e2aa1","abstract_canon_sha256":"d2dcc633ec677abedf2a6a562070d7d2f8bd410b7a3bb19ef232df8681f3d3cc"},"schema_version":"1.0"},"canonical_sha256":"c37650df7ab21e9f3f8f5e2faf55bea7fb620ce16c25fd048d44cf65cd43ed4c","source":{"kind":"arxiv","id":"1806.09594","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.09594","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"arxiv_version","alias_value":"1806.09594v2","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.09594","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"pith_short_12","alias_value":"YN3FBX32WIPJ","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YN3FBX32WIPJ6P4P","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YN3FBX32","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:YN3FBX32WIPJ6P4PLYX26VN6U7","target":"record","payload":{"canonical_record":{"source":{"id":"1806.09594","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-25T17:44:40Z","cross_cats_sorted":["cs.GR","cs.LG","cs.MM","cs.RO"],"title_canon_sha256":"91019568e4700a1ed0c4a081bf99d964258b125750cce4b45827770fbc2e2aa1","abstract_canon_sha256":"d2dcc633ec677abedf2a6a562070d7d2f8bd410b7a3bb19ef232df8681f3d3cc"},"schema_version":"1.0"},"canonical_sha256":"c37650df7ab21e9f3f8f5e2faf55bea7fb620ce16c25fd048d44cf65cd43ed4c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:37.492897Z","signature_b64":"R+WAlk+7bEI1JG2Vpa75Aiqrs0imnw+VB9BxrYCY8w0h8ylen7+YX7GuJl46NwOaxHIJMKO+YEoYl4Sg5byHDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c37650df7ab21e9f3f8f5e2faf55bea7fb620ce16c25fd048d44cf65cd43ed4c","last_reissued_at":"2026-05-18T00:09:37.492250Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:37.492250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.09594","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":"2026-05-18T00:09:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fnFP5+vcfbEzVu93EoiAQZZ2FLpjb/fCd2U3Z8MKUAy8ENgVC+VAdB8zYm3qijcfrbEHR9jMmN/pxq/89sjoAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T23:27:56.680773Z"},"content_sha256":"5644ecbcea7ca6680108db8d20461a6b32f7b0821132232f9e09a299e975068b","schema_version":"1.0","event_id":"sha256:5644ecbcea7ca6680108db8d20461a6b32f7b0821132232f9e09a299e975068b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:YN3FBX32WIPJ6P4PLYX26VN6U7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tracking Emerges by Colorizing Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG","cs.MM","cs.RO"],"primary_cat":"cs.CV","authors_text":"Abhinav Shrivastava, Alireza Fathi, Carl Vondrick, Kevin Murphy, Sergio Guadarrama","submitted_at":"2018-06-25T17:44:40Z","abstract_excerpt":"We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-scale videos by copying colors from a reference frame. Quantitative and qualitative experiments suggest that this task causes the model to automatically learn to track visual regions. Although the model is trained without any ground-truth labels, our method learns to track well enough to outperform the latest methods based on optical flow. Moreover, our results suggest that failures to tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.09594","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":"2026-05-18T00:09:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3IG0YETP6/JgNBrc7pDK1z0d8XSxHcwF8pwHGnv6nB6UviOBJSBe58bl6eUyXv3xHOfosetmfU0QrUC+3R+/AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T23:27:56.681444Z"},"content_sha256":"6ca48245bf35eac88da887265880ccb819d4c1fea2606fbd41527932e37d8e03","schema_version":"1.0","event_id":"sha256:6ca48245bf35eac88da887265880ccb819d4c1fea2606fbd41527932e37d8e03"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YN3FBX32WIPJ6P4PLYX26VN6U7/bundle.json","state_url":"https://pith.science/pith/YN3FBX32WIPJ6P4PLYX26VN6U7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YN3FBX32WIPJ6P4PLYX26VN6U7/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":"2026-05-19T23:27:56Z","links":{"resolver":"https://pith.science/pith/YN3FBX32WIPJ6P4PLYX26VN6U7","bundle":"https://pith.science/pith/YN3FBX32WIPJ6P4PLYX26VN6U7/bundle.json","state":"https://pith.science/pith/YN3FBX32WIPJ6P4PLYX26VN6U7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YN3FBX32WIPJ6P4PLYX26VN6U7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:YN3FBX32WIPJ6P4PLYX26VN6U7","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":"d2dcc633ec677abedf2a6a562070d7d2f8bd410b7a3bb19ef232df8681f3d3cc","cross_cats_sorted":["cs.GR","cs.LG","cs.MM","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-25T17:44:40Z","title_canon_sha256":"91019568e4700a1ed0c4a081bf99d964258b125750cce4b45827770fbc2e2aa1"},"schema_version":"1.0","source":{"id":"1806.09594","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.09594","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"arxiv_version","alias_value":"1806.09594v2","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.09594","created_at":"2026-05-18T00:09:37Z"},{"alias_kind":"pith_short_12","alias_value":"YN3FBX32WIPJ","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YN3FBX32WIPJ6P4P","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YN3FBX32","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:6ca48245bf35eac88da887265880ccb819d4c1fea2606fbd41527932e37d8e03","target":"graph","created_at":"2026-05-18T00:09:37Z","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 use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-scale videos by copying colors from a reference frame. Quantitative and qualitative experiments suggest that this task causes the model to automatically learn to track visual regions. Although the model is trained without any ground-truth labels, our method learns to track well enough to outperform the latest methods based on optical flow. Moreover, our results suggest that failures to tr","authors_text":"Abhinav Shrivastava, Alireza Fathi, Carl Vondrick, Kevin Murphy, Sergio Guadarrama","cross_cats":["cs.GR","cs.LG","cs.MM","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-25T17:44:40Z","title":"Tracking Emerges by Colorizing Videos"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.09594","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":"sha256:5644ecbcea7ca6680108db8d20461a6b32f7b0821132232f9e09a299e975068b","target":"record","created_at":"2026-05-18T00:09:37Z","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":"d2dcc633ec677abedf2a6a562070d7d2f8bd410b7a3bb19ef232df8681f3d3cc","cross_cats_sorted":["cs.GR","cs.LG","cs.MM","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-25T17:44:40Z","title_canon_sha256":"91019568e4700a1ed0c4a081bf99d964258b125750cce4b45827770fbc2e2aa1"},"schema_version":"1.0","source":{"id":"1806.09594","kind":"arxiv","version":2}},"canonical_sha256":"c37650df7ab21e9f3f8f5e2faf55bea7fb620ce16c25fd048d44cf65cd43ed4c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c37650df7ab21e9f3f8f5e2faf55bea7fb620ce16c25fd048d44cf65cd43ed4c","first_computed_at":"2026-05-18T00:09:37.492250Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:37.492250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R+WAlk+7bEI1JG2Vpa75Aiqrs0imnw+VB9BxrYCY8w0h8ylen7+YX7GuJl46NwOaxHIJMKO+YEoYl4Sg5byHDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:37.492897Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.09594","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5644ecbcea7ca6680108db8d20461a6b32f7b0821132232f9e09a299e975068b","sha256:6ca48245bf35eac88da887265880ccb819d4c1fea2606fbd41527932e37d8e03"],"state_sha256":"fb007a4be3927e1618a1bd694dec0cc809b7c8c08dc941bca244c89f810370b7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bDXkHjaaluNVzJv7BGg06PST7ZB+0+LHg0q+eyI1NBNVZ/vNPygpx/ReMkJF9aqLfmzVhiK/mevVDTBCzh+0BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T23:27:56.685374Z","bundle_sha256":"35d6a5780d131550ef4549e015727b4612de4ed4226da2ddd737e5e012edb3db"}}