AI 中文总结
研究针对开放世界视觉智能挑战，提出基于生物视觉和信息瓶颈理论的神经形态视觉系统，通过信息蒸馏策略及硬件集成实现任务导向视觉智能，在多场景任务中准确率提升且延迟大幅降低。
AI 中文摘要
在非结构化开放世界环境中，高效且稳健的视觉智能仍是关键挑战，当前方法常依赖计算密集型神经架构或通用性有限的特定任务传感器。受生物视觉和信息瓶颈理论启发，我们报告了一种神经形态视觉系统，它通过在硬件上实现的信息蒸馏策略（任务牵引机制）执行面向任务的视觉智能。该系统将偏振敏感成像器与电阻式随机存取存储器（RRAM）阵列集成，通过光场选择、感兴趣区域提取和目标预测逐步提炼与任务相关的信息。该神经形态视觉系统在193微秒的执行时间内完成视觉任务。在八个具有挑战性的开放世界场景中的评估表明，在目标跟踪、目标分割和轨迹预测方面，准确率分别提高了25.54%、37.73%和36.10%，相对于现有技术解决方案，延迟平均降低了30.6倍。
英文摘要
Time-efficient and robust visual intelligence remains a critical challenge in unstructured open-world environments, yet current approaches often rely on computationally intensive neural architectures or task-specific sensors with limited versatility. Inspired by biological vision and information bottleneck theory, we report a neuromorphic vision system that performs task-oriented visual intelligence through an information distillation strategy (named as task traction mechanism) implemented on hardware. The system integrates a polarization-sensitive imager with a resistive random-access memory (RRAM) array to progressively distill task-relevant information via light field selection, region of interest extraction, and target anticipation. The neuromorphic vision system conducts visual tasks within an execution time of 193 μs. Evaluation across eight challenging open-world scenarios shows accuracy improvements of 25.54%, 37.73%, and 36.10% for object tracking, object segmentation, and trajectory prediction, respectively, together with an average 30.6-fold reduction in latency relative to state-of-the-art solutions.
