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[R] TextVQA Challenge: Close the large gap between human accuracy and state-of-the-art.

Dataset Website: https://textvqa.org

Challenge Link: https://evalai.cloudcv.org/web/challenges/challenge-page/244/overview

Prize: $10k GCP Credits

Starter Code: https://github.com/facebookresearch/pythia

Paper: https://arxiv.org/abs/1904.08920

Deadline: 18th May (ask for extension if needed)

More details on the challenge: https://textvqa.org/challenge

Explore the dataset: https://textvqa.org/explore

Detailed Description:Current state-of-the-art VQA models are unable to read and reason about text in images which in contrast is most asked by the users of such systems. TextVQA aims to provide a benchmark for measuring progress of VQA models on text reading and reasoning capabilities.

State-of-the-art VQA models on TextVQA are only around 14% while the human accuracy is ~85%. LoRRA module introduced in TextVQA paper can be attached to any VQA model to add text reading and reasoning capabilities. The current state-of-the-art on TextVQA is ~27% with LoRRA.

Use the starter code to participate in challenge to win $10k GCP credits and help close this large gap.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.