  Visual dialogue is a challenging task since it needs to answer a series of
coherent questions on the basis of understanding the visual environment.
Previous studies focus on the implicit exploration of multimodal co-reference
by implicitly attending to spatial image features or object-level image
features but neglect the importance of locating the objects explicitly in the
visual content, which is associated with entities in the textual content.
Therefore, in this paper we propose a {\bf M}ultimodal {\bf I}ncremental {\bf
T}ransformer with {\bf V}isual {\bf G}rounding, named MITVG, which consists of
two key parts: visual grounding and multimodal incremental transformer. Visual
grounding aims to explicitly locate related objects in the image guided by
textual entities, which helps the model exclude the visual content that does
not need attention. On the basis of visual grounding, the multimodal
incremental transformer encodes the multi-turn dialogue history combined with
visual scene step by step according to the order of the dialogue and then
generates a contextually and visually coherent response. Experimental results
on the VisDial v0.9 and v1.0 datasets demonstrate the superiority of the
proposed model, which achieves comparable performance.
