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Towards VIA Models That Can Read Amandeep Singh1, Vivek Catamaran, Meet Shah1, You Jiang1, Linda Chen1, Drug Batra1,2, Devi Parikh1,2, and Marcus Rohrbach1 1Facebook AI Research, 2 Georgia Institute
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Prepare the dataset with image-question pair annotations.
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Preprocess the images and questions.
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Train the VQA model using the prepared dataset.
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Evaluate the trained model on the validation set.
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Repeat the above steps for different VQA models if required.

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Towards VQA (Visual Question Answering) models refer to frameworks and algorithms designed to enable machines to understand and generate answers to questions posed about images or visual content.
Individuals or organizations that utilize or develop VQA models in a regulatory context may be required to file necessary documentation regarding their models, though the exact entities depend on jurisdiction and the specific guidelines set forth by relevant authorities.
Filling out forms related to VQA models typically involves providing details about the model architecture, datasets used for training and evaluation, performance metrics, and compliance with any relevant standards.
Information that must be reported includes model specifications, data sources, ethical considerations, testing results, and any potential biases identified during the development process.
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