Visual question answering (VQA) is an interdisciplinary direction in the field of computer vision and natural language processing. It has received extensive attention in recent years. In the visual question answering, the algorithm is required to answer questions based on specific pictures (or videos). Since the first visual question answering dataset was released in 2014, several large-scale datasets have been released in the past five years, and a large number of algorithms have been proposed based on them. Existing research has focused on the development of visual question answering, but in recent years, visual question answering has been found to rely heavily on language bias and the distribution of datasets, especially since the release of the VQA-CP dataset, the accuracy of many models has been greatly reduced. This paper mainly introduces the proposed algorithms and the released datasets in recent years, especially discusses the research of algorithms on strengthening the robustness. The algorithms of visual question answering are summarized and their motivation, details, and limitations are also introduced. Finally, the challenge and prospect of visual question answering are discussed.