earticle

영어

Visual Question Answering (VQA) models suffer from a language bias problem, where they excessively rely on textual correlations. This study proposes a plausible counterfactual data generation method, named Plausible Counterfactual Data Generation (PCDG), which utilizes Grad- CAM-based visual importance to replace significant objects in a contextually appropriate manner. By synthesizing more contextually relevant samples than other existing augmentation methods, PCDS effectively strengthens visual-language alignment. In experiments on the VQA-CP v2 benchmark, our method achieved significant performance improvements, particularly a 10.56% increase in the 'Num' category and a 2.78% increase in the 'Other' category. This indicates that the proposed method enhances the VQA model's generalization ability and robustness through debiasing.