2022
DOI: 10.48550/arxiv.2206.14355
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EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering

Abstract: Context. The availability of clean and diverse labeled data is a major roadblock for training models on complex tasks such as visual question answering (VQA). The extensive work on large vision-and-language models has shown that self-supervised learning is effective for pretraining multimodal interactions. In this technical report, we focus on visual representations. We review and evaluate self-supervised methods to leverage unlabeled images and pretrain a model, which we then fine-tune on a custom VQA task th… Show more

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