Attention mechanisms have been widely used in Visual Question Answering (VQA)
solutions due to their capacity to model deep cross-domain interactions.
Analyzing attention maps offers us a perspective to find out limitations of
current VQA systems and an opportunity to further improve them. In this paper,
we select two state-of-the-art VQA approaches with attention mechanisms to
study their robustness and disadvantages by visualizing and analyzing their
estimated attention maps. We find that both methods are sensitive to features,
and simultaneously, they perform badly for counting and multi-object related
questions. We believe that the findings and analytical method will help
researchers identify crucial challenges on the way to improve their own VQA
systems.

本文结合注意力机制提出了两种最先进的视觉问答方法，并通过可视化和分析它们的估计注意力图来研究它们的鲁棒性和缺点。研究表明两种方法对特征敏感，同时对于计数和多对象相关的问题表现不佳。该研究结果和分析方法可帮助研究人员识别重要的挑战，以改进自己的 VQA 系统。