We present two practical improvement techniques for unsupervised segmentation learning. These techniques address limitations in the resolution and accuracy of predicted segmentation maps of recent state-of-the-art methods. Firstly, we leverage image post-processing techniques such as guided filtering to refine the output masks, improving accuracy while avoiding substantial computational costs. Secondly, we introduce a multi-scale consistency criterion, based on a teacher-student training scheme. This criterion matches segmentation masks predicted from regions of the input image extracted at different resolutions to each other. Experimental results on several benchmarks used in unsupervised segmentation learning demonstrate the effectiveness of our proposed techniques.
@article{arxiv.2404.03392,
title = {Boosting Unsupervised Segmentation Learning},
author = {Alp Eren Sari and Francesco Locatello and Paolo Favaro},
journal= {arXiv preprint arXiv:2404.03392},
year = {2024}
}
Comments
Accepted to NeurIPS 2024 Workshop: Self-Supervised Learning - Theory and Practice