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Visual Question Answering (VQA) task has showcased a new stage of interactionbetween language and vision, two of the most pivotal components of artificialintelligence. However, it has mostly focused on generating short and repetitiveanswers, mostly single words, which fall short of rich linguistic capabilitiesof humans. We introduce Full-Sentence Visual Question Answering (FSVQA)dataset, consisting of nearly 1 million pairs of questions and full-sentenceanswers for images, built by applying a number of rule-based natural languageprocessing techniques to original VQA dataset and captions in the MS COCOdataset. This poses many additional complexities to conventional VQA task, andwe provide a baseline for approaching and evaluating the task, on top of whichwe invite the research community to build further improvements.

The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)