  Raven's Progressive Matrices (RPMs) are frequently used in testing human's
visual reasoning ability. Recent advances of RPM-like datasets and solution
models partially address the challenges of visually understanding the RPM
questions and logically reasoning the missing answers. In view of the poor
generalization performance due to insufficient samples in RPM datasets, we
propose an effective scheme, namely Candidate Answer Morphological Mixup
(CAM-Mix). CAM-Mix serves as a data augmentation strategy by gray-scale image
morphological mixup, which regularizes various solution methods and overcomes
the model overfitting problem. By creating new negative candidate answers
semantically similar to the correct answers, a more accurate decision boundary
could be defined. By applying the proposed data augmentation method, a
significant and consistent performance improvement is achieved on various
RPM-like datasets compared with the state-of-the-art models.
