Visual question answering (VQA) is a task that combines both the techniques of computer vision and natural language processing. It requires models to answer a text-based question according to the information contained in a visual. In recent years, the research field of VQA has been expanded. Research that focuses on the VQA, examining the reasoning ability and VQA on scientific diagrams, has also been explored more. Meanwhile, more multimodal feature fusion mechanisms have been proposed. This paper will review and analyze existing datasets, metrics, and models proposed for the VQA task.
@article{arxiv.2105.00421,
title = {A survey on VQA_Datasets and Approaches},
author = {Yeyun Zou and Qiyu Xie},
journal= {arXiv preprint arXiv:2105.00421},
year = {2021}
}