The image, question (combined with the history for de-referencing), and the
corresponding answer are three vital components of visual dialog. Classical
visual dialog systems integrate the image, question, and history to search for
or generate the best matched answer, and so, this approach significantly
ignores the role of the answer. In this paper, we devise a novel
image-question-answer synergistic network to value the role of the answer for
precise visual dialog. We extend the traditional one-stage solution to a
two-stage solution. In the first stage, candidate answers are coarsely scored
according to their relevance to the image and question pair. Afterward, in the
second stage, answers with high probability of being correct are re-ranked by
synergizing with image and question. On the Visual Dialog v1.0 dataset, the
proposed synergistic network boosts the discriminative visual dialog model to
achieve a new state-of-the-art of 57.88\% normalized discounted cumulative
gain. A generative visual dialog model equipped with the proposed technique
also shows promising improvements.

本研究提出了一种图像 - 问题 - 答案协同网络来评估答案的作用，该网络将传统的一阶段解决方案扩展为两个阶段，并在 Visual Dialog v1.0 数据集中取得了最新的最高成果。