行动识别与深度卷积神经网络的人类视觉途径:在晚期但不是早期层中的表示对应性
Yujia Peng1,2,3,4, Xizi Gong1, Hongjing Lu4,5
1School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, People's Republic of China.
Journal of cognitive neuroscience
|August 6, 2024
概括
深层卷积神经网络 (DCNNs) 在视频中的动作识别中不与人类大脑排序一致. 后来的DCNN层与人类视觉皮层有更多的相似之处.
科学领域:
- 神经科学是一个神经科学.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 深层卷积神经网络 (DCNNs) 实现了人类级别的对象识别.
- 之前的研究表明,DCNN与静态图像任务的大脑区域之间的对齐.
- 这种对齐的扩展到动态视觉任务,如动作识别仍然未被探索.
研究的目的:
- 研究从视频中识别动作时,双流DCNN与人类大脑区域的表示对齐.
- 将DCNN表示与沿腹部和背部视觉通路的大脑活动进行比较.
主要方法:
- 利用解码分析来比较DCNN模型表示与大脑数据.
- 采用代表性相似性分析 (RSA) 来评估视觉区域之间的对齐.
- 测试模型在两个摄影现实化的化身视频和简化的点灯显示刺激.
主要成果:
- 在DCNN模型中,对于动作视频处理,并没有与人类大脑的等级表示对齐.
- 后来的DCNN模型层显示出与人类视觉皮层的代表性相似性增加.
- 这种模式适用于光现实和点灯显示格式.
结论:
- 在静态图像识别中观察到的等级表示对齐不适用于视频中的动作识别.
- DCNN和人类大脑可能采用从根本上不同的策略来表示动态视觉信息,特别是行动.
- 后期的DCNN层更好地捕获视觉皮层对动作刺激的表示.
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