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Different from Visual Question Answering task that requires to answer onlyone question about an image, Visual Dialogue involves multiple questions whichcover a broad range of visual content that could be related to any objects,relationships or semantics. The key challenge in Visual Dialogue task is thusto learn a more comprehensive and semantic-rich image representation which mayhave adaptive attentions on the image for variant questions. In this research,we propose a novel model to depict an image from both visual and semanticperspectives. Specifically, the visual view helps capture the appearance-levelinformation, including objects and their relationships, while the semantic viewenables the agent to understand high-level visual semantics from the wholeimage to the local regions. Futhermore, on top of such multi-view imagefeatures, we propose a feature selection framework which is able to adaptivelycapture question-relevant information hierarchically in fine-grained level. Theproposed method achieved state-of-the-art results on benchmark Visual Dialoguedatasets. More importantly, we can tell which modality (visual or semantic) hasmore contribution in answering the current question by visualizing the gatevalues. It gives us insights in understanding of human cognition in VisualDialogue.

DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue