All You May Need for VQA are Image Captions

• URL: http://arxiv.org/abs/2205.01883v1
• Date: Wed, 4 May 2022 04:09:23 GMT
• Title: All You May Need for VQA are Image Captions
• Authors: Soravit Changpinyo, Doron Kukliansky, Idan Szpektor, Xi Chen, Nan Ding, Radu Soricut
• Abstract summary: We propose a method that automatically derives VQA examples at volume. 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.
• Score: 24.634567673906666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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