Image captioning improved visual question answering

Both Visual Question Answering (VQA) and image captioning are the problems which involve Computer Vision (CV) and Natural Language Processing (NLP) domains. In general, computer vision models are effectively utilized to represent visual contents. While NLP algorithms are used to represent the sentences. In recent years, VQA and image captioning tasks are tackled independently although they require similar type of algorithms. In this paper, a joint relationship between these two tasks is established and exploited. We present an image captioning based VQA model that uses the knowledge learnt from the image captioning task and transfers that knowledge to VQA task. We integrate the image captioning module into the VQA model by fusing the features obtained from captioning model and the attention-based visual feature. The experimental results demonstrate the improvement in the answer generation accuracy by a margin 3.45 % on VQA 1.0, 3.33% on VQA 2.0 and 1.73% on VQA-CP v2 datasets over the state-of-the-art VQA models.