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arXiv · 1906.00513

Generating Question Relevant Captions to Aid Visual Question Answering

Jialin Wu·Zeyuan Hu·Raymond J. Mooney
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Abstract

Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to improve VQA performance that exploits this connection by jointly generating captions that are targeted to help answer a specific visual question. The model is trained using an existing caption dataset by automatically determining question-relevant captions using an online gradient-based method. Experimental results on the VQA v2 challenge demonstrates that our approach obtains state-of-the-art VQA performance (e.g. 68.4% on the Test-standard set using a single model) by simultaneously generating question-relevant captions.

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Jialin Wu, Zeyuan Hu, Raymond J. Mooney. 2019-06-03. Generating Question Relevant Captions to Aid Visual Question Answering. https://arxiv.org/abs/1906.00513

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