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We propose $\mathbb{VD}$-$\mathbb{GR}$ - a novel visual dialog model thatcombines pre-trained language models (LMs) with graph neural networks (GNNs).Prior works mainly focused on one class of models at the expense of the other,thus missing out on the opportunity of combining their respective benefits. Atthe core of $\mathbb{VD}$-$\mathbb{GR}$ is a novel integration mechanism thatalternates between spatial-temporal multi-modal GNNs and BERT layers, and thatcovers three distinct contributions: First, we use multi-modal GNNs to processthe features of each modality (image, question, and dialog history) and exploittheir local structures before performing BERT global attention. Second, wepropose hub-nodes that link to all other nodes within one modality graph,allowing the model to propagate information from one GNN (modality) to theother in a cascaded manner. Third, we augment the BERT hidden states withfine-grained multi-modal GNN features before passing them to the next$\mathbb{VD}$-$\mathbb{GR}$ layer. Evaluations on VisDial v1.0, VisDial v0.9,VisDialConv, and VisPro show that $\mathbb{VD}$-$\mathbb{GR}$ achieves newstate-of-the-art results across all four datasets.

$\mathbb{VD}$-$\mathbb{GR}$: Boosting $\mathbb{V}$isual $\mathbb{D}$ialog with Cascaded Spatial-Temporal Multi-Modal $\mathbb{GR}$aphs