Humans are continuously exposed to a stream of visual data with a natural temporal structure. However, most successful computer vision algorithms work at image level, completely discarding the precious information carried by motion. In this paper, we claim that processing visual streams naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of learning that originates from variational principles, just like in physics. Such principled approach is well suited for a discussion on a number of interesting questions that arise in vision, and it offers a well-posed computational scheme for the discovery of convolutional filters over the retina. Differently from traditional convolutional networks, which need massive supervision, the proposed theory offers a truly new scenario for the unsupervised processing of video signals, where features are extracted in a multi-layer architecture with motion invariance. While the theory enables the implementation of novel computer vision systems, it also sheds light on the role of information-based principles to drive possible biological solutions.

中文翻译：

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在运动不变性下学习视觉特征。

人类不断暴露于具有自然时间结构的视觉数据流。但是，大多数成功的计算机视觉算法都在图像级别工作，完全丢弃了运动携带的宝贵信息。在本文中，我们声称处理视觉流自然会导致制定运动不变性原理，这使得能够构建源自变分原理的新学习理论，就像在物理学中一样。这种有原则的方法非常适合讨论视觉中出现的许多有趣的问题，并且它为发现视网膜上的卷积滤镜提供了一个恰当的计算方案。与需要大量监管的传统卷积网络不同，所提出的理论为视频信号的无监督处理提供了一个真正的新方案，即在具有运动不变性的多层体系结构中提取特征。虽然该理论可以实现新颖的计算机视觉系统，但它也阐明了基于信息的原理在驱动可能的生物学解决方案中的作用。