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 answ

Context

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. http://vizwiz.org/pics/vqa-examples.jpg" alt="vizwiz">

Content

Visual questions are split into three JSON files: train, validation, and test. Answers are publicly shared for the train and validation splits and hidden for the test split. APIs are provided to demonstrate how to parse the JSON files and evaluate methods against the ground truth.

• 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

Acknowledgements

This dataset is from the challenge VizWiz Challenge

Inspiration

I bring this dataset to kaggle because i want to help the blind people and to do it I need help and a lot of people