{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:LNF6OD2TMZQBQRYQKIUMZOCUJZ","short_pith_number":"pith:LNF6OD2T","canonical_record":{"source":{"id":"2005.06536","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-13T19:05:42Z","cross_cats_sorted":[],"title_canon_sha256":"40082ef3f06ca53a8ecea3eb5baf1555c12c981d10cdffc54ad7a4c28b7ed7c0","abstract_canon_sha256":"73230ec1b42ee0cf57afb76656f2bce1f72559b98c94262f76eb1a27f520d164"},"schema_version":"1.0"},"canonical_sha256":"5b4be70f5366601847105228ccb8544e64f2674e9533df94356b93b9c8854f73","source":{"kind":"arxiv","id":"2005.06536","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.06536","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"arxiv_version","alias_value":"2005.06536v1","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.06536","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"pith_short_12","alias_value":"LNF6OD2TMZQB","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"pith_short_16","alias_value":"LNF6OD2TMZQBQRYQ","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"pith_short_8","alias_value":"LNF6OD2T","created_at":"2026-07-05T01:02:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:LNF6OD2TMZQBQRYQKIUMZOCUJZ","target":"record","payload":{"canonical_record":{"source":{"id":"2005.06536","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-13T19:05:42Z","cross_cats_sorted":[],"title_canon_sha256":"40082ef3f06ca53a8ecea3eb5baf1555c12c981d10cdffc54ad7a4c28b7ed7c0","abstract_canon_sha256":"73230ec1b42ee0cf57afb76656f2bce1f72559b98c94262f76eb1a27f520d164"},"schema_version":"1.0"},"canonical_sha256":"5b4be70f5366601847105228ccb8544e64f2674e9533df94356b93b9c8854f73","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:02:47.649049Z","signature_b64":"s4I/pNo11y3fgqTHpMTmFPGiXWromVUyYDbAawp9ALfUH5n6qpqY5ZjCF3ibHzhwbVBR6OqMPbRjNVLlA3+eCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b4be70f5366601847105228ccb8544e64f2674e9533df94356b93b9c8854f73","last_reissued_at":"2026-07-05T01:02:47.648592Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:02:47.648592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.06536","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":"2026-07-05T01:02:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u3ipP/5o1OV7/XM2fEjbWyrdDsdu/q8i0u735O5Zpg4S3EWi/CAQ4NElvNU2NvzIZGqIvk3ZHgLl5ORY6tEACw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T07:41:27.446092Z"},"content_sha256":"9265598e080b233840314ebc521248a099effded668aa82bdbea5125e7007995","schema_version":"1.0","event_id":"sha256:9265598e080b233840314ebc521248a099effded668aa82bdbea5125e7007995"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:LNF6OD2TMZQBQRYQKIUMZOCUJZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dan Zeng, Jingen Liu, Ke Wang, Ning Zhang, Tao Mei","submitted_at":"2020-05-13T19:05:42Z","abstract_excerpt":"The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised training data in offline mode. However, most of them can still fail in tracking objects due to some more challenging issues such as dense distractor objects, confusing background, motion blurs, and so on. Inspired by the human \"visual tracking\" capability which leverages motion cues to distinguish the target from the background, we propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking, whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.06536","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.06536/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:02:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PwQKQvKO3cFJob0gHqsf6g0xVjvDujt+UMvrq0cQg4lJqsQW1QCiPJTC3s7H8LEDoC7PPVwI42abzwXY/ow1Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T07:41:27.446477Z"},"content_sha256":"a2f1bf6ff2240a27ef0920a18c877dbc6df9dd63b460667d2e93f91cf970d207","schema_version":"1.0","event_id":"sha256:a2f1bf6ff2240a27ef0920a18c877dbc6df9dd63b460667d2e93f91cf970d207"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LNF6OD2TMZQBQRYQKIUMZOCUJZ/bundle.json","state_url":"https://pith.science/pith/LNF6OD2TMZQBQRYQKIUMZOCUJZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LNF6OD2TMZQBQRYQKIUMZOCUJZ/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":"2026-07-24T07:41:27Z","links":{"resolver":"https://pith.science/pith/LNF6OD2TMZQBQRYQKIUMZOCUJZ","bundle":"https://pith.science/pith/LNF6OD2TMZQBQRYQKIUMZOCUJZ/bundle.json","state":"https://pith.science/pith/LNF6OD2TMZQBQRYQKIUMZOCUJZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LNF6OD2TMZQBQRYQKIUMZOCUJZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:LNF6OD2TMZQBQRYQKIUMZOCUJZ","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":"73230ec1b42ee0cf57afb76656f2bce1f72559b98c94262f76eb1a27f520d164","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-13T19:05:42Z","title_canon_sha256":"40082ef3f06ca53a8ecea3eb5baf1555c12c981d10cdffc54ad7a4c28b7ed7c0"},"schema_version":"1.0","source":{"id":"2005.06536","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.06536","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"arxiv_version","alias_value":"2005.06536v1","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.06536","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"pith_short_12","alias_value":"LNF6OD2TMZQB","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"pith_short_16","alias_value":"LNF6OD2TMZQBQRYQ","created_at":"2026-07-05T01:02:47Z"},{"alias_kind":"pith_short_8","alias_value":"LNF6OD2T","created_at":"2026-07-05T01:02:47Z"}],"graph_snapshots":[{"event_id":"sha256:a2f1bf6ff2240a27ef0920a18c877dbc6df9dd63b460667d2e93f91cf970d207","target":"graph","created_at":"2026-07-05T01:02:47Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2005.06536/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised training data in offline mode. However, most of them can still fail in tracking objects due to some more challenging issues such as dense distractor objects, confusing background, motion blurs, and so on. Inspired by the human \"visual tracking\" capability which leverages motion cues to distinguish the target from the background, we propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking, whi","authors_text":"Dan Zeng, Jingen Liu, Ke Wang, Ning Zhang, Tao Mei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-13T19:05:42Z","title":"Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.06536","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":"sha256:9265598e080b233840314ebc521248a099effded668aa82bdbea5125e7007995","target":"record","created_at":"2026-07-05T01:02:47Z","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":"73230ec1b42ee0cf57afb76656f2bce1f72559b98c94262f76eb1a27f520d164","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-13T19:05:42Z","title_canon_sha256":"40082ef3f06ca53a8ecea3eb5baf1555c12c981d10cdffc54ad7a4c28b7ed7c0"},"schema_version":"1.0","source":{"id":"2005.06536","kind":"arxiv","version":1}},"canonical_sha256":"5b4be70f5366601847105228ccb8544e64f2674e9533df94356b93b9c8854f73","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b4be70f5366601847105228ccb8544e64f2674e9533df94356b93b9c8854f73","first_computed_at":"2026-07-05T01:02:47.648592Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:02:47.648592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s4I/pNo11y3fgqTHpMTmFPGiXWromVUyYDbAawp9ALfUH5n6qpqY5ZjCF3ibHzhwbVBR6OqMPbRjNVLlA3+eCA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:02:47.649049Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.06536","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9265598e080b233840314ebc521248a099effded668aa82bdbea5125e7007995","sha256:a2f1bf6ff2240a27ef0920a18c877dbc6df9dd63b460667d2e93f91cf970d207"],"state_sha256":"db743be4bd52911f2abd395925edb690cc4c3658a7199c257f0ae93a0115523a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IMUfw618W9Pz/DoEW1ef+Ufo37t9jY98+xFv9MPWCaMIck6itrdRXbj5kkYIdBVZTswslIyDJcpQSUJ4O4rKAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T07:41:27.448664Z","bundle_sha256":"d783d1e8c0e2bf01bfea729df1d50d8a40da09ea1b14e8b4657b05126d48fbbb"}}