  Visual question answering is the task of answering questions about images. We
introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually
grounds answers to visual questions asked by people with visual impairments. We
analyze our dataset and compare it with five VQA-Grounding datasets to
demonstrate what makes it similar and different. We then evaluate the SOTA VQA
and VQA-Grounding models and demonstrate that current SOTA algorithms often
fail to identify the correct visual evidence where the answer is located. These
models regularly struggle when the visual evidence occupies a small fraction of
the image, for images that are higher quality, as well as for visual questions
that require skills in text recognition. The dataset, evaluation server, and
leaderboard all can be found at the following link:
https://vizwiz.org/tasks-and-datasets/answer-grounding-for-vqa/.
