Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image, using the conversation history as context. It entails challenges in vision, language, reasoning, and grounding. However, studying these subtasks in isolation on large, real datasets is infeasible as it requires prohibitively-expensive complete annotation of the 'state' of all images and dialogs. We develop CLEVR-Dialog, a large diagnostic dataset for studying multi-round reasoning in visual dialog. Specifically, we construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset. This combination results in a dataset where all aspects of the visual dialog are fully annotated. In total, CLEVR-Dialog contains 5 instances of 10-round dialogs for about 85k CLEVR images, totaling to 4.25M question-answer pairs. We use CLEVR-Dialog to benchmark performance of standard visual dialog models; in particular, on visual coreference resolution (as a function of the coreference distance). This is the first analysis of its kind for visual dialog models that was not possible without this dataset. We hope the findings from CLEVR-Dialog will help inform the development of future models for visual dialog. Our dataset and code will be made public.

开发了一个名为CLEVR-Dialog的大型诊断数据集，以研究视觉对话中的多轮推理（multiround reasoning），在其中所有视觉对话的所有方面都得到了完全注释。使用CLEVR-Dialog测量了标准视觉对话模型的性能，特别是针对视觉指代消解（coreference resolution）的表现，并于之前数据集中的模型进行了比较，发现了其中的差异。

CLEVR-Dialog: 一个用于视觉对话多轮推理的诊断数据集