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Existing Visual Question Answering (VQA) methods tend to exploit datasetbiases and spurious statistical correlations, instead of producing rightanswers for the right reasons. To address this issue, recent bias mitigationmethods for VQA propose to incorporate visual cues (e.g., human attention maps)to better ground the VQA models, showcasing impressive gains. However, we showthat the performance improvements are not a result of improved visualgrounding, but a regularization effect which prevents over-fitting tolinguistic priors. For instance, we find that it is not actually necessary toprovide proper, human-based cues; random, insensible cues also result insimilar improvements. Based on this observation, we propose a simplerregularization scheme that does not require any external annotations and yetachieves near state-of-the-art performance on VQA-CPv2.

Visual Grounding Methods for VQA are Working for the Wrong Reasons!