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