  Visual Commonsense Reasoning (VCR) remains a significant yet challenging
research problem in the realm of visual reasoning. A VCR model generally aims
at answering a textual question regarding an image, followed by the rationale
prediction for the preceding answering process. Though these two processes are
sequential and intertwined, existing methods always consider them as two
independent matching-based instances. They, therefore, ignore the pivotal
relationship between the two processes, leading to sub-optimal model
performance. This paper presents a novel visual attention alignment method to
efficaciously handle these two processes in a unified framework. To achieve
this, we first design a re-attention module for aggregating the vision
attention map produced in each process. Thereafter, the resultant two sets of
attention maps are carefully aligned to guide the two processes to make
decisions based on the same image regions. We apply this method to both
conventional attention and the recent Transformer models and carry out
extensive experiments on the VCR benchmark dataset. The results demonstrate
that with the attention alignment module, our method achieves a considerable
improvement over the baseline methods, evidently revealing the feasibility of
the coupling of the two processes as well as the effectiveness of the proposed
method.
