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Visual Dialog is a multimodal task of answering a sequence of questionsgrounded in an image, using the conversation history as context. It entailschallenges in vision, language, reasoning, and grounding. However, studyingthese subtasks in isolation on large, real datasets is infeasible as itrequires prohibitively-expensive complete annotation of the 'state' of allimages and dialogs.  We develop CLEVR-Dialog, a large diagnostic dataset for studying multi-roundreasoning in visual dialog. Specifically, we construct a dialog grammar that isgrounded in the scene graphs of the images from the CLEVR dataset. Thiscombination results in a dataset where all aspects of the visual dialog arefully annotated. In total, CLEVR-Dialog contains 5 instances of 10-rounddialogs for about 85k CLEVR images, totaling to 4.25M question-answer pairs.  We use CLEVR-Dialog to benchmark performance of standard visual dialogmodels; in particular, on visual coreference resolution (as a function of thecoreference distance). This is the first analysis of its kind for visual dialogmodels that was not possible without this dataset. We hope the findings fromCLEVR-Dialog will help inform the development of future models for visualdialog. Our dataset and code are publicly available.

CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog