“Recent years have seen a lot of efforts in attacking the issue of language priors in the field ofVisual Question Answering (VQA). Among the extensive efforts, causal inference is regarded asa promising direction to mitigate language bias by weakening the direct causal effect of questionson answers. In this paper, we follow the same direction and attack the issue of language priorsby incorporating counterfactual data. Moreover, we propose a two-stage training strategy whichis deemed to make better use of counterfactual data. Experiments on the widely used bench-mark VQA-CP v2 demonstrate the effectiveness of the proposed approach, which improves thebaseline by 21.21% and outperforms most of the previous systems.”