Data augmentation by morphological mixup for solving Raven's Progressive Matrices
This work addresses generalization issues in visual reasoning tasks for AI models, but it is incremental as it builds on existing data augmentation techniques.
The paper tackled the problem of poor generalization in solving Raven's Progressive Matrices due to insufficient data, and achieved significant and consistent performance improvements on various RPM-like datasets using a data augmentation method called CAM-Mix.
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.