{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MF56XMFBUZB6GXJXYRDY4F4FSF","short_pith_number":"pith:MF56XMFB","schema_version":"1.0","canonical_sha256":"617bebb0a1a643e35d37c4478e1785915a3ce07e1ea5a6e24ccb1457ac2c54d4","source":{"kind":"arxiv","id":"2503.08703","version":4},"attestation_state":"computed","paper":{"title":"SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.NE","authors_text":"Dehao Zhang, Haicheng Qu, Haodi Wu, Jason K. Eshraghian, Jieyuan Zhang, Jingzhinan Wang, Kexin Shi, Malu Zhang, Rui-Jie Zhu, Shuai Wang, Wenjie Wei, Yichen Xiao, Yimeng Shan, Zhenbang Ren","submitted_at":"2025-03-09T02:01:40Z","abstract_excerpt":"Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based \\textbf{S}pike-\\textbf{D}riven \\textbf{T}racking (SDTrack) pipeline. It incorporates a novel event fr"},"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":"2503.08703","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NE","submitted_at":"2025-03-09T02:01:40Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"ad997ebaa3e366126f0f3f1e582a26300a2a4dd23be6d2a678563d1796514486","abstract_canon_sha256":"6659ff65ecbe8c63147b91944b66bdd75d718a36f56157c18a8a8cc2e8832e42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:04:38.141347Z","signature_b64":"XQL09SbjAu80fFMOQWsr6c3hcpZd55j8drFVoOyFUif56IbdbkHo0amRlqoTA1kW3ZF7BnKT0+bdrjEhWNdfDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"617bebb0a1a643e35d37c4478e1785915a3ce07e1ea5a6e24ccb1457ac2c54d4","last_reissued_at":"2026-06-09T01:04:38.140763Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:04:38.140763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.NE","authors_text":"Dehao Zhang, Haicheng Qu, Haodi Wu, Jason K. Eshraghian, Jieyuan Zhang, Jingzhinan Wang, Kexin Shi, Malu Zhang, Rui-Jie Zhu, Shuai Wang, Wenjie Wei, Yichen Xiao, Yimeng Shan, Zhenbang Ren","submitted_at":"2025-03-09T02:01:40Z","abstract_excerpt":"Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based \\textbf{S}pike-\\textbf{D}riven \\textbf{T}racking (SDTrack) pipeline. It incorporates a novel event fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.08703","kind":"arxiv","version":4},"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/2503.08703/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2503.08703","created_at":"2026-06-09T01:04:38.140844+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.08703v4","created_at":"2026-06-09T01:04:38.140844+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.08703","created_at":"2026-06-09T01:04:38.140844+00:00"},{"alias_kind":"pith_short_12","alias_value":"MF56XMFBUZB6","created_at":"2026-06-09T01:04:38.140844+00:00"},{"alias_kind":"pith_short_16","alias_value":"MF56XMFBUZB6GXJX","created_at":"2026-06-09T01:04:38.140844+00:00"},{"alias_kind":"pith_short_8","alias_value":"MF56XMFB","created_at":"2026-06-09T01:04:38.140844+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/MF56XMFBUZB6GXJXYRDY4F4FSF","json":"https://pith.science/pith/MF56XMFBUZB6GXJXYRDY4F4FSF.json","graph_json":"https://pith.science/api/pith-number/MF56XMFBUZB6GXJXYRDY4F4FSF/graph.json","events_json":"https://pith.science/api/pith-number/MF56XMFBUZB6GXJXYRDY4F4FSF/events.json","paper":"https://pith.science/paper/MF56XMFB"},"agent_actions":{"view_html":"https://pith.science/pith/MF56XMFBUZB6GXJXYRDY4F4FSF","download_json":"https://pith.science/pith/MF56XMFBUZB6GXJXYRDY4F4FSF.json","view_paper":"https://pith.science/paper/MF56XMFB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.08703&json=true","fetch_graph":"https://pith.science/api/pith-number/MF56XMFBUZB6GXJXYRDY4F4FSF/graph.json","fetch_events":"https://pith.science/api/pith-number/MF56XMFBUZB6GXJXYRDY4F4FSF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MF56XMFBUZB6GXJXYRDY4F4FSF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MF56XMFBUZB6GXJXYRDY4F4FSF/action/storage_attestation","attest_author":"https://pith.science/pith/MF56XMFBUZB6GXJXYRDY4F4FSF/action/author_attestation","sign_citation":"https://pith.science/pith/MF56XMFBUZB6GXJXYRDY4F4FSF/action/citation_signature","submit_replication":"https://pith.science/pith/MF56XMFBUZB6GXJXYRDY4F4FSF/action/replication_record"}},"created_at":"2026-06-09T01:04:38.140844+00:00","updated_at":"2026-06-09T01:04:38.140844+00:00"}