  Visual question answering (VQA) is a challenging task, which has attracted
more and more attention in the field of computer vision and natural language
processing. However, the current visual question answering has the problem of
language bias, which reduces the robustness of the model and has an adverse
impact on the practical application of visual question answering. In this
paper, we conduct a comprehensive review and analysis of this field for the
first time, and classify the existing methods according to three categories,
including enhancing visual information, weakening language priors, data
enhancement and training strategies. At the same time, the relevant
representative methods are introduced, summarized and analyzed in turn. The
causes of language bias are revealed and classified. Secondly, this paper
introduces the datasets mainly used for testing, and reports the experimental
results of various existing methods. Finally, we discuss the possible future
research directions in this field.
