MUREL: Multimodal Relational Reasoning for Visual Question Answering
Table 5: State-of-the-art comparison on the VQA-CP v2 dataset. The Attention model was trained by us using the Bottom-up features.
['Model', 'Bottom', 'Yes/No', 'Num.', 'Other', 'All']
S4SS3SSS0Px3 VQA-CP v2 This dataset has been proposed to evaluate and reduce the question-oriented bias in VQA models. In particular, the distributions of answers with respect to question types differ from train to val splits. No questions. However, since both methods do not use the Bottom-up features, the fairness of the comparison can be questioned. We observe that MuRel provides a substantial gain over this strong attention baseline. Given the distribution mismatch between train and val splits, models that only focus on linguistic biases to answer the question are systematically penalized on their val scores. This property of VQA-CP v2 implies that the pairwise iterative structure of MuRel is less prone to question-based overfitting than classical attention architectures.