Computer Science > Computer Vision and Pattern Recognition
[Submitted on 4 May 2022]
Title:All You May Need for VQA are Image Captions
View PDFAbstract:Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. In this paper, we propose a method that automatically derives VQA examples at volume, by leveraging the abundance of existing image-caption annotations combined with neural models for textual question generation. We show that the resulting data is of high-quality. VQA models trained on our data improve state-of-the-art zero-shot accuracy by double digits and achieve a level of robustness that lacks in the same model trained on human-annotated VQA data.
Submission history
From: Soravit Changpinyo [view email][v1] Wed, 4 May 2022 04:09:23 UTC (2,157 KB)
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