Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
It addresses the problem of semantic segmentation without labeled data for computer vision researchers, representing a significant advance over prior unsupervised methods.
The paper tackles unsupervised semantic segmentation by using self-supervised object-centric representations, achieving 50.0 mIoU on PASCAL VOC and discovering 34 categories with over 20% IoU on MS COCO.
In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and self-supervised feature clustering to kickstart object discovery followed by training a semantic segmentation network on pseudo-labels to bootstrap the system on images with multiple objects. We present results on PASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we report for the first time results on MS COCO for the whole set of 81 classes: our method discovers 34 categories with more than $20\%$ IoU, while obtaining an average IoU of 19.6 for all 81 categories.