A 345mW Heterogeneous Many-Core Processor with an Intelligent Inference Engine for Robust Object Recognition

📋 论文概要

该论文提出了一款345mW的异构众核处理器，集成了智能推理引擎，用于解决鲁棒目标识别中的高计算需求和背景杂波导致的误匹配问题。通过改进视觉注意力机制，提高了识别准确率。

💡 主要创新点

• 提出了一种智能推理引擎，结合自底向上的视觉注意力机制，提高了目标识别的鲁棒性。
• 采用异构众核架构，在低功耗（345mW）下实现高计算性能。
• 通过优化注意力准确性，减少了背景杂波对识别精度的负面影响。

🏷 关键词

📄 原文摘要

challenges: (1) the large number of features to process requires high computational power, and (2) false matches from background clutter can degrade recognition accuracy. Previously, saliency based bottom-up visual attention [1,2] increased recognition speed by confining the recognition processing only to the salient regions. But these schemes had an inherent problem: the accuracy of the attention itself. If attention is paid to the false region, which is common when saliency cannot distinguish between clutter and object, recognition accuracy is degraded. In order to improve the attention accuracy, we previously reported an algorithm, the Unified Visual Attention Model (UVAM) [3], which incorporates the familiarity map on top of the saliency map for the search of attentive points. It can cross-check the accuracy of attention deployment by combining top-down

👥 作者与机构

Seungjin Lee, Jinwook Oh, Minsu Kim, Junyoung Park, Joonsoo Kwon, Hoi-Jun Yoo

KAIST, Daejeon, Korea Fast and robust object recognition of cluttered scenes presents two main