  Existing Visual Question Answering (VQA) methods tend to exploit dataset
biases and spurious statistical correlations, instead of producing right
answers for the right reasons. To address this issue, recent bias mitigation
methods for VQA propose to incorporate visual cues (e.g., human attention maps)
to better ground the VQA models, showcasing impressive gains. However, we show
that the performance improvements are not a result of improved visual
grounding, but a regularization effect which prevents over-fitting to
linguistic priors. For instance, we find that it is not actually necessary to
provide proper, human-based cues; random, insensible cues also result in
similar improvements. Based on this observation, we propose a simpler
regularization scheme that does not require any external annotations and yet
achieves near state-of-the-art performance on VQA-CPv2.
