Learning vector representation of local content and matrix representation of local motion, with implications for V1

Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu

Abstract: This paper proposes a representational model for image pair such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the following two components. (1) The vector representations of local contents of images. (2) The matrix representations of local pixel displacements caused by the relative motions between the agent and the objects in the 3D scene. When the image frame undergoes changes due to local pixel displacements, the vectors are multiplied by the matrices that represent the local displacements. Our experiments show that our model can learn to infer local motions. Moreover, the model can learn Gabor-like filter pairs of quadrature phases.

Keywords: Representation learning, V1, neuroscience