Self-supervised sub-category exploration for Pseudo label generation

Abstract

Image segmentation-based applications have been actively investigated. However, it is non-trivial to prepare polygon annotations. Previous studies suggested pseudo label generation methods based on weakly supervised learning to lessen the burden of annotation. Nevertheless, the quality of pseudo labels could not be ideal due to target object characteristics and insufficient data size in the construction domain, as identified in this study. This study proposes a fusion architecture, SESC-CAM, to address the challenge, building upon weakly and self-supervised learning methods. The proposed architecture was validated on the AIM dataset, and the generated pseudo labels recorded a mIoU score of 64.99% and 67.65% after the refinement by using a conditional random field, and outperformed its predecessors by 11.29% and 9.14%. The refined pseudo labels were used to train a segmentation model and recorded a 74% mIoU score in semantic segmentation results. The findings of this study provide insights for automated training data preparation.