一个双-外周视觉处理模型实现了高效的saccade选择
Emmanuel Daucé1,2, Pierre Albiges1,3, Laurent U Perrinet1,4
1Institut de Neurosciences de la Timone (UMR 7289), Aix Marseille University, CNRS, Marseille, France.
Journal of vision
|May 17, 2024
概括
我们开发了一种用于视觉搜索的新视觉运动模型,将目标检测范围提高了十倍. 这种人工智能方法使用不同的"什么"和"哪里"网络,灵感来自于人类视觉,以实现高效的对象本地化.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 人类的视觉搜索涉及到对象识别 ("什么") 和空间定位 ("哪里") 的专门途径.
- 当前的计算机视觉模型通常在处理完整视野时缺乏仿生效率.
研究的目的:
- 开发一种模仿人类视觉搜索策略的视觉运动模型.
- 提高人工系统中视觉搜索的效率和范围.
主要方法:
- 一个双路径模型,用于视的"什么"网络 (深度学习分类器) 和用于外周视的"哪里"网络 (日志极点编码).
- 一个演员-关键框架,其中准确度训练"Where"网络,以生成指导眼动 (saccades) 的准确性地图.
- 在杂乱的图像中测试数字查找任务.
主要成果:
- 该模型显示可检测和可识别目标的半径增加了十倍.
- 逻辑极性编码使视觉信息的亚线性处理成为可能,超过了主流计算机视觉方法.
- "何处"网络成功地引导眼睛的运动向外围物体.
结论:
- 拟议的视觉运动模型有效地整合了"什么"和"哪里"信息,以实现高效的视觉搜索.
- 生物仿真方法,特别是日志极性编码,为大规模的视觉搜索任务提供了显著的优势.
- 这个框架提供了一个新的计算方法来理解和复制人类的视觉注意力.
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