mlsanityData to 7 Oct 2026

VizWiz Grand Challenge: Answering Visual Questions from Blind People

arXiv 1802.08218 ↗First posted 22 Feb 2018cs.CV cs.CL cs.HCcited by 852

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Cited by 501 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 852 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2018.

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Citing papers per year: 2021: 29; 2022: 40; 2023: 98; 2024: 256; 2025: 253; 2026: 176.

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