@article{,
title= {VizWiz v1.0 dataset (Answering Visual Questions from Blind People)},
keywords= {},
author= {},
abstract= {We propose an artificial intelligence challenge to design algorithms that assist people who are blind to overcome their daily visual challenges. For this purpose, we introduce the VizWiz dataset, which originates from a natural visual question answering setting where blind people each took an image and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. Our proposed challenge addresses the following two tasks for this dataset: (1) predict the answer to a visual question and (2) predict whether a visual question cannot be answered. Ultimately, we hope this work will educate more people about the technological needs of blind people while providing an exciting new opportunity for researchers to develop assistive technologies that eliminate accessibility barriers for blind people.

```
VizWiz v1.0 dataset download:

20,000 training image/question pairs
200,000 training answer/answer confidence pairs
3,173 image/question pairs
31,730 validation answer/answer confidence pairs
8,000 image/question pairs
Python API to read and visualize the VizWiz dataset
Python challenge evaluation code
```

![](https://i.imgur.com/zXB6Qci.png)


### Publications

Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P. Bigham. "VizWiz Grand Challenge: Answering Visual Questions from Blind People." IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

Jeffrey P. Bigham, Chandrika Jayant, Hanjie Ji, Greg Little, Andrew Miller, Robert C. Miller, Robin Miller, Aubrey Tatarowicz, Brandyn White, Samuel White, and Tom Yeh. "VizWiz: Nearly Real-time Answers to Visual Questions." ACM User Interface Software and Technology Symposium (UIST), 2010.},
terms= {},
license= {Creative Commons Attribution-ShareAlike 4.0 International License},
superseded= {},
url= {http://vizwiz.org/}
}