New Datasets and Models for Contextual Reasoning in Visual Dialog

Visual Dialog (VD) is a vision-language task that requires AI systems to maintain a natural question-answering dialog about visual contents. Using the dialog history as contexts, VD models have achieved promising performance on public benchmarks. However, prior VD datasets do not provide sufficient contextually dependent questions that require knowledge from the dialog history to answer. As a result, advanced VQA models can still perform well without considering the dialog context. In this work, we focus on developing new datasets and models to highlight the role of contextual reasoning in VD. We define a hierarchy of contextual patterns to represent and organize the dialog context, enabling quantitative analyses of contextual dependencies and designs of new VD datasts and models. We then develop two new datasets, namely CLEVR-VD and GQA-VD, offering context-rich dialogs over synthetic and realistic images, respectively. Furthermore, we propose a novel neural module network method featuring contextual reasoning in VD. We demonstrate the effectiveness of our proposed datasets and method with experimental results and model comparisons across different datasets. Our code and data are available at https://github.com/SuperJohnZhang/ContextVD.