用类脑脉冲时序可塑性实现高效事件相机目标检测，无需标注和显卡。
Brain-inspired spike-timing plasticity for reliable label-efficient event-camera vision
- 设计三种局部脉冲时序可塑模块，单线程运行不依赖GPU。
- 零标注下达53.8% mAP@30，有少量标注时达78.6%。
- 适用于资源受限场景，适合事件相机与类脑计算研究者。
部署事件相机目标检测受每帧标注需求和GPU算力限制。本文提出三种局部脉冲时序依赖可塑性（STDP）模块：序列、候选与管段可靠性模块，可在单一CPU线程上运行且无需GPU支持。在FRED无人机基准上，该框架覆盖三个标签高效监督层级：严格零标注检测器达到53.8% mAP@30；约26个训练衍生比特下达76.9% mAP@30；STDP候选可靠性门实现78.60±0.42% mAP@30。在采集顺序漂移下，群体门优于流式k-means 2.03±0.58个百分点（20/20正向试验），无漂移对照组验证了该效应。STDP使单模型方差降低6.6倍，一个训练好的门可匹配44种子集集成性能。该门在Intel Lava上转移后保持89%前二名一致性。在EVUAV基准上，管级STDP层将误报率从454降至331e-4（Pd ≥ 88%）。密集梯度训练检测器因结构限制无法同时实现梯度训练、密集矩阵运算与无本地可塑性操作。
原文摘要 · Abstract (English)
Deploying event-camera object detectors is constrained by per-frame labeling requirements and GPU compute demands. This work introduces three local spike-timing-dependent plasticity (STDP) modules, including sequence, candidate, and tube-reliability modules, that operate on a single CPU thread without GPU support. On the FRED drone benchmark, the proposed framework spans three label-efficient supervision tiers. A strict zero-label detector achieves 53.8% mAP@30, approximately 26 train-derived bits achieve 76.9% mAP@30, and an STDP candidate-reliability gate achieves 78.60 +/- 0.42% mAP@30. Under acquisition-order drift, the cohort gate outperforms streaming k-means by 2.03 +/- 0.58 percentage points across 20 of 20 positive trials, while a no-drift control falsifies the effect. STDP reduces single-model variance by 6.6 times, and one trained gate matches a 44-seed ensemble bound. The gate transfers to Intel Lava with 89% top-2 agreement. On the EVUAV benchmark, a tube-level STDP layer reduces false alarms from 454 to 331e-4 at Pd >= 88%. Dense gradient-trained detectors cannot provide this combination of gradient training, dense matrix multiplication, and local plasticity-free operation by construction.
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