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Most existing research on visual question answering (VQA) is limited toinformation explicitly present in an image or a video. In this paper, we takevisual understanding to a higher level where systems are challenged to answerquestions that involve mentally simulating the hypothetical consequences ofperforming specific actions in a given scenario. Towards that end, we formulatea vision-language question answering task based on the CLEVR (Johnson et. al.,2017) dataset. We then modify the best existing VQA methods and proposebaseline solvers for this task. Finally, we motivate the development of bettervision-language models by providing insights about the capability of diversearchitectures to perform joint reasoning over image-text modality. Our datasetsetup scripts and codes will be made publicly available athttps://github.com/shailaja183/clevr_hyp.

CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over Images