Different from Visual Question Answering task that requires to answer only
one question about an image, Visual Dialogue involves multiple questions which
cover a broad range of visual content that could be related to any objects,
relationships or semantics. The key challenge in Visual Dialogue task is thus
to learn a more comprehensive and semantic-rich image representation which may
have adaptive attentions on the image for variant questions. In this research,
we propose a novel model to depict an image from both visual and semantic
perspectives. Specifically, the visual view helps capture the appearance-level
information, including objects and their relationships, while the semantic view
enables the agent to understand high-level visual semantics from the whole
image to the local regions. Futhermore, on top of such multi-view image
features, we propose a feature selection framework which is able to adaptively
capture question-relevant information hierarchically in fine-grained level. The
proposed method achieved state-of-the-art results on benchmark Visual Dialogue
datasets. More importantly, we can tell which modality (visual or semantic) has
more contribution in answering the current question by visualizing the gate
values. It gives us insights in understanding of human cognition in Visual
Dialogue.

该研究提出了一种新的模型来从视觉和语义两个角度描述图像，在多角度图像特征的基础上提出了特征选择框架，逐层适应性地捕捉问题相关信息，并在基准视觉对话数据集上取得了最先进的结果。更重要的是，通过可视化门控值，我们能够确定视觉和语义哪个模式在回答当前问题中发挥更重要的作用，为我们理解人类认知在视觉对话中的作用提供了见解。