 In this paper, we propose a novel cause-effect look at the language bias , where the bias is formulated as the direct  effect of question on answer from the view of causal inference. The effectcan be captured by counterfactual VQA, where the image had not existed in an imagined scenario. Our proposed cause-effect look 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
<coherence>  In this paper, we propose a novel cause-effect look at the language bias , where the bias is formulated as the direct  effect of question on answer from the view of causal inference. The effectcan be captured by counterfactual VQA, where the image had not existed in an imagined scenario. Our proposed cause-effect look 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .
 In this paper, we propose a novel cause-effect look at the language bias , where the bias is formulated as the direct effect . Experiments demonstrate that our proposed counterfactual inference framework 1) is general to any baseline VQA architecture , 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset , and 3) fills the theoretical gap in recent language prior based works .