  Raven's Progressive Matrices (RPMs) are frequently-used in testing human's
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.
