English(EN) Event-based Selective Attention for Multi-resolution Fast Region of Interest (ROI) Detection

新型注意力模型提升脉冲视觉效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一种新颖的事件驱动选择性注意力模型，专为资源受限的边缘设备上的脉冲视觉系统设计。该模型处理低分辨率的事件驱动输入，将数据量最多减少256倍，以识别感兴趣区域（ROIs）。在Prophesee Automotive数据集上进行评估，该方法在毫秒级时间分辨率下，对车辆和行人等各类物体类别实现了高达70.8%的准确率，展现了强大的ROI选择能力。 AI

影响该模型有望在边缘设备上实现更高效、更强大的脉冲视觉系统。

排序理由这是一篇详细介绍计算机视觉领域新颖技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Luca Peres, Giulia D'Angelo, Chiara Bartolozzi, Oliver Rhodes · 2026-09-16 04:00

面向多分辨率感兴趣区域（ROI）检测的事件驱动选择性注意力机制

arXiv:2609.17134v1 Announce Type: new Abstract: Neuromorphic vision systems operate under strict constraints on bandwidth, memory, and energy, particularly at the edge, motivating early mechanisms for data reduction and selective processing. In this work, we investigate a multi-s…

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面向多分辨率感兴趣区域（ROI）检测的事件驱动选择性注意力机制

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