Visual attention, which assigns weights to image regions according to their relevance to a question, is considered as an indispensable part by most Visual Question Answering models. Although the questions may involve complex relations among multiple regions, few attention models can effectively encode such cross-region relations. In this paper, we demonstrate the importance of encoding such relations by showing the limited effective receptive field of ResNet on two datasets, and propose to model the visual attention as a multivariate distribution over a grid-structured Conditional Random Field on image regions. We demonstrate how to convert the iterative inference algorithms, Mean Field and Loopy Belief Propagation, as recurrent layers of an end-to-end neural network. We empirically evaluated our model on 3 datasets, in which it surpasses the best baseline model of the newly released CLEVR dataset by 9.5%, and the best published model on the VQA dataset by 1.25%. Source code is available at https: //github.com/zhuchen03/vqa-sva.

本文提出通过构建基于网格结构条件随机场上的多变量分布的视觉注意力模型来有效地编码复杂的跨区域关系，并将迭代推理算法转换成端到端的神经网络层，该模型在3个数据集上的实验评估结果表明，它超过了新发布的CLEVR数据集的最佳基线模型9.5％，并超过了VQA数据集上最佳发布模型1.25％。

视觉问答的结构型注意力