{"paper":{"title":"BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Burst spikes and dual-channel attention improve accuracy in spiking vision transformers without sacrificing energy efficiency.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dewei Bai, Hong Qu, Hongxiang Peng","submitted_at":"2026-04-25T06:33:04Z","abstract_excerpt":"Spiking Vision Transformers (S-ViTs) offer a promising framework for energy-efficient visual learning. However, existing designs remain limited by two fundamental issues: the restricted information capacity of binary spike coding and the dense token interactions introduced by global self-attention. To address these challenges, this work proposes BSViT, a burst spiking-driven Vision Transformer featuring a Dual-Channel Burst Spiking Self-Attention (DBSSA) mechanism. DBSSA encodes queries with binary spikes and keys with burst spikes to enhance representational capacity. The value pathway adopts"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Extensive experiments on both static and event-based vision benchmarks demonstrate that BSViT consistently outperforms existing spiking Transformers in accuracy while maintaining competitive energy efficiency.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That encoding queries with binary spikes, keys with burst spikes, and values with dual excitatory/inhibitory binary channels will increase representational capacity and spike interactions while preserving addition-only computation and hardware compatibility.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"BSViT introduces burst spike coding and dual-channel burst spiking self-attention in a Vision Transformer, outperforming prior spiking transformers on static and event-based vision tasks with competitive energy efficiency.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Burst spikes and dual-channel attention improve accuracy in spiking vision transformers without sacrificing energy efficiency.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b3901281e8c719d79a2e06a4f1dadbb546725bd79f72c74ada6978ea8451a39b"},"source":{"id":"2604.23165","kind":"arxiv","version":2},"verdict":{"id":"fc6d6b58-8b5c-4516-9b4d-a9b7127d09b0","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T08:31:41.534091Z","strongest_claim":"Extensive experiments on both static and event-based vision benchmarks demonstrate that BSViT consistently outperforms existing spiking Transformers in accuracy while maintaining competitive energy efficiency.","one_line_summary":"BSViT introduces burst spike coding and dual-channel burst spiking self-attention in a Vision Transformer, outperforming prior spiking transformers on static and event-based vision tasks with competitive energy efficiency.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That encoding queries with binary spikes, keys with burst spikes, and values with dual excitatory/inhibitory binary channels will increase representational capacity and spike interactions while preserving addition-only computation and hardware compatibility.","pith_extraction_headline":"Burst spikes and dual-channel attention improve accuracy in spiking vision transformers without sacrificing energy efficiency."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.23165/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T09:38:13.009615Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T23:24:32.113815Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"93985dd619c658160b0316430b56dc9c672a5f0df604c75701d93d5765fd3438"},"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"}