Heterogeneous Knowledge Network for Visual Dialog

Abstract

Visual dialog requires an agent to answer successive questions considering an image and dialog history, which is a classic vision-language task. Despite progress, there are still two key challenges: 1) parsing long or complex questions and answers and 2) dealing with the visual scene containing complicated interactions among entities. These challenges bring about the unsatisfactory consequence of current visual dialog methods. In this paper, we propose a novel Heterogeneous Knowledge Network (HKNet), which leverages textual sequence knowledge and graph knowledge to address the above issues. Specifically, the textual sequence knowledge is derived from the sentences that are retrieved from the image captions of the visual dialog dataset. The textual sequence knowledge can supplement essential common sense for parsing long or complex questions and answers. The graph knowledge is constructed via scene graph, which provides complete visual relationships for understanding the complicated interactions. These two kinds of heterogeneous knowledge complement each other and jointly improve the logical reasoning ability of the visual dialog. Extensive experimental results on two benchmark datasets: VisDial v0.9 and v1.0 demonstrate the superiority of the proposed HKNet. Ablation studies and visualization results further verify the effectiveness of our method.