Abstract
Inferring the relations between two images is an important class of tasks in computer vision. Examples of such tasks include computing optical flow and stereo disparity. We treat the relation inference tasks as a machine learning problem and tackle it with neural networks. A key to the problem is learning a representation of relations. We propose a new neural network module, contrast association unit (CAU), which explicitly models the relations between two sets of input variables. Due to the non-negativity of the weights in CAU, we adopt a multiplicative update algorithm for learning these weights. Experiments show that neural networks with CAUs are more effective in learning five fundamental image transformations than conventional neural networks.
Abstract (translated)
推断两幅图像之间的关系是计算机视觉中的一个重要任务。这些任务的例子包括计算光流和立体视差。我们将关系推理任务视为机器学习问题，并用神经网络进行处理。问题的关键是学习关系的表示。我们提出了一个新的神经网络模块，对比关联单元（CAU），它明确地模拟了两组输入变量之间的关系。由于CAU中权重的非负性，我们采用乘法更新算法来学习这些权重。实验表明，与传统的神经网络相比，带有CAUS的神经网络更能有效地学习五种基本图像变换。
URL
https://arxiv.org/abs/1705.05665
