Visual dialogue is a challenging task that needs to extract implicit information from both visual (image) and textual (dialogue history) contexts. Classical approaches pay more attention to the integration of the current question, vision knowledge and text knowledge, despising the heterogeneous semantic gaps between the cross-modal information. In the meantime, the concatenation operation has become de-facto standard to the cross-modal information fusion, which has a limited ability in information retrieval. In this paper, we propose a novel Knowledge-Bridge Graph Network (KBGN) model by using graph to bridge the cross-modal semantic relations between vision and text knowledge in fine granularity, as well as retrieving required knowledge via an adaptive information selection mode. Moreover, the reasoning clues for visual dialogue can be clearly drawn from intra-modal entities and inter-modal bridges. Experimental results on VisDial v1.0 and VisDial-Q datasets demonstrate that our model outperforms exiting models with state-of-the-art results.

本文提出了一种新颖的知识桥梁图网络模型，该模型使用图形在细粒度上构建跨模式语义关系的桥梁，通过自适应信息选择模式检索所需的知识，从而清晰地提取视觉对话的推理线索，并通过实验结果在VisDial v1.0和VisDial-Q数据集上展示了超越现有模型的最先进成果。

KBGN：基于知识桥接图网络的视觉-文本问答自适应推理