Dialog is an effective way to exchange information, but subtle details and
nuances are extremely important. While significant progress has paved a path to
address visual dialog with algorithms, details and nuances remain a challenge.
Attention mechanisms have demonstrated compelling results to extract details in
visual question answering and also provide a convincing framework for visual
dialog due to their interpretability and effectiveness. However, the many data
utilities that accompany visual dialog challenge existing attention techniques.
We address this issue and develop a general attention mechanism for visual
dialog which operates on any number of data utilities. To this end, we design a
factor graph based attention mechanism which combines any number of utility
representations. We illustrate the applicability of the proposed approach on
the challenging and recently introduced VisDial datasets, outperforming recent
state-of-the-art methods by 1.1% for VisDial0.9 and by 2% for VisDial1.0 on
MRR. Our ensemble model improved the MRR score on VisDial1.0 by more than 6%.

本研究提出了基于因子图的注意力机制，结合任意数量的数据工具对视觉对话进行建模，提高视觉对话系统的性能表现。实验结果表明，该方法在当前领先技术上提升了 1.1％和 2％的 MRR 分数，并且在 VisDial1.0 上的集成模型提高了超过 6％的 MRR 分数。