  Visual Dialog is a vision-language task that requires an AI agent to engage
in a conversation with humans grounded in an image. It remains a challenging
task since it requires the agent to fully understand a given question before
making an appropriate response not only from the textual dialog history, but
also from the visually-grounded information. While previous models typically
leverage single-hop reasoning or single-channel reasoning to deal with this
complex multimodal reasoning task, which is intuitively insufficient. In this
paper, we thus propose a novel and more powerful Dual-channel Multi-hop
Reasoning Model for Visual Dialog, named DMRM. DMRM synchronously captures
information from the dialog history and the image to enrich the semantic
representation of the question by exploiting dual-channel reasoning.
Specifically, DMRM maintains a dual channel to obtain the question- and
history-aware image features and the question- and image-aware dialog history
features by a mulit-hop reasoning process in each channel. Additionally, we
also design an effective multimodal attention to further enhance the decoder to
generate more accurate responses. Experimental results on the VisDial v0.9 and
v1.0 datasets demonstrate that the proposed model is effective and outperforms
compared models by a significant margin.
