{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:UGRVY2RIWS7HRINXPABP7HF6S7","short_pith_number":"pith:UGRVY2RI","schema_version":"1.0","canonical_sha256":"a1a35c6a28b4be78a1b77802ff9cbe97ec7f122f9b68f5286eedac203e433578","source":{"kind":"arxiv","id":"1801.02021","version":1},"attestation_state":"computed","paper":{"title":"Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Wang, Gang Wang, Li Wang, Ting Liu, Xulei Yang","submitted_at":"2018-01-06T14:39:29Z","abstract_excerpt":"Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only available in the first frame of a test sequence. In this paper, we propose to learn hierarchical features for visual object tracking by using tree structure based Recursive Neural Networks (RNN), which have fewer parameters than other deep neural networks, e.g. Convolutional Neural"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1801.02021","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-06T14:39:29Z","cross_cats_sorted":[],"title_canon_sha256":"5fa2cba6c8aaf31369495e141665f8bc4d386f201dde7340d307636ebe193587","abstract_canon_sha256":"fdcb0652c0638c1b68b7f63069470d4d4846e942bf6b60fdada34653e3e5407d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:35.159756Z","signature_b64":"6DtWQEpfCRCmITM2ZRZcK9ci40jWWq0srv/bywt8NrMQs4a53/BK8qtSajLmsFlfPElxsKOmfTgZ4BMYCrogCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1a35c6a28b4be78a1b77802ff9cbe97ec7f122f9b68f5286eedac203e433578","last_reissued_at":"2026-05-18T00:26:35.159056Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:35.159056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Wang, Gang Wang, Li Wang, Ting Liu, Xulei Yang","submitted_at":"2018-01-06T14:39:29Z","abstract_excerpt":"Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only available in the first frame of a test sequence. In this paper, we propose to learn hierarchical features for visual object tracking by using tree structure based Recursive Neural Networks (RNN), which have fewer parameters than other deep neural networks, e.g. Convolutional Neural"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.02021","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1801.02021","created_at":"2026-05-18T00:26:35.159148+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.02021v1","created_at":"2026-05-18T00:26:35.159148+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.02021","created_at":"2026-05-18T00:26:35.159148+00:00"},{"alias_kind":"pith_short_12","alias_value":"UGRVY2RIWS7H","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"UGRVY2RIWS7HRINX","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"UGRVY2RI","created_at":"2026-05-18T12:32:56.356000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7","json":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7.json","graph_json":"https://pith.science/api/pith-number/UGRVY2RIWS7HRINXPABP7HF6S7/graph.json","events_json":"https://pith.science/api/pith-number/UGRVY2RIWS7HRINXPABP7HF6S7/events.json","paper":"https://pith.science/paper/UGRVY2RI"},"agent_actions":{"view_html":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7","download_json":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7.json","view_paper":"https://pith.science/paper/UGRVY2RI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.02021&json=true","fetch_graph":"https://pith.science/api/pith-number/UGRVY2RIWS7HRINXPABP7HF6S7/graph.json","fetch_events":"https://pith.science/api/pith-number/UGRVY2RIWS7HRINXPABP7HF6S7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7/action/storage_attestation","attest_author":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7/action/author_attestation","sign_citation":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7/action/citation_signature","submit_replication":"https://pith.science/pith/UGRVY2RIWS7HRINXPABP7HF6S7/action/replication_record"}},"created_at":"2026-05-18T00:26:35.159148+00:00","updated_at":"2026-05-18T00:26:35.159148+00:00"}