VizWiz Grand Challenge: Answering Visual Questions from Blind People
This dataset addresses the need for more generalized VQA algorithms to assist blind people, representing a domain-specific advancement.
The authors tackled the problem of visual question answering (VQA) by introducing VizWiz, a dataset of over 31,000 visual questions from blind people, which revealed that modern algorithms struggle with its challenges, such as poor-quality images and unanswerable questions.
The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising from a natural VQA setting. VizWiz consists of over 31,000 visual questions originating from blind people who each took a picture using a mobile phone and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. VizWiz differs from the many existing VQA datasets because (1) images are captured by blind photographers and so are often poor quality, (2) questions are spoken and so are more conversational, and (3) often visual questions cannot be answered. Evaluation of modern algorithms for answering visual questions and deciding if a visual question is answerable reveals that VizWiz is a challenging dataset. We introduce this dataset to encourage a larger community to develop more generalized algorithms that can assist blind people.