Étude comparative des méthodes semi-supervisées pour la segmentation sémantique

Abstract

During the last few years, many breakthroughs in the field of semi-supervised learning have proven to be very effective in overcoming the lack of labeled images caused by the high cost of pixel-level labeling. Numerous approaches exploring the use of both labeled and unlabeled images have been published. This thesis explores the current state of the art in semi-supervised semantic segmentation, highlighting experimental results, current challenges, and future research directions in this field.