Ravens Progressive Matrices (RPMs) are frequently-used in testing humans visual reasoning ability. Recently developed RPM-like datasets and solution models transfer this kind of problems from cognitive science to computer science. In view of the poor generalization performance due to insufficient samples in RPM datasets, we propose a data augmentation strategy by image mix-up, which is generalizable to a variety of multiple-choice problems, especially for image-based RPM-like problems. By focusing on potential functionalities of negative candidate answers, the visual reasoning capability of the model is enhanced. By applying the proposed data augmentation method, we achieve significant and consistent improvement on various RPM-like datasets compared with the state-of-the-art models.
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