关于动态识别模型，人类最小视频能告诉我们什么？
What can human minimal videos tell us about dynamic recognition models?
摘要：
在人类视觉中，物体及其部分可以从纯粹的空间或纯粹的时间信息中视觉识别，但整合空间和时间的机制却知之甚少。在这里，我们表明，人类的视觉识别的对象和动作，可以通过有效地结合空间和运动线索的配置中，每个源本身是不够的识别。这种分析是通过识别最小视频来实现的：这些视频是短而小的视频片段，其中的对象、部件和动作可以被可靠地识别，但空间或时间的任何减少都会使它们无法识别。用于动态视觉识别的最先进的深度网络无法在这些配置中复制人类行为。人类和机器之间的这种差距表明了当前模型中缺乏的人类动态视觉中的关键机制。
英文摘要：
In human vision objects and their parts can be visually recognized from purely spatial or purely temporal information but the mechanisms integrating space and time are poorly understood. Here we show that human visual recognition of objects and actions can be achieved by efficiently combining spatial and motion cues in configurations where each source on its own is insufficient for recognition. This analysis is obtained by identifying minimal videos: these are short and tiny video clips in which objects, parts, and actions can be reliably recognized, but any reduction in either space or time makes them unrecognizable. State-of-the-art deep networks for dynamic visual recognition cannot replicate human behavior in these configurations. This gap between humans and machines points to critical mechanisms in human dynamic vision that are lacking in current models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
