In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch
from semi-supervised classification, where the prediction of a weakly perturbed image …

As a mainstream framework in the field of semi-supervised learning (SSL), self-training via
pseudo labeling and its variants have witnessed impressive progress in semi-supervised …

Recent weakly supervised semantic segmentation (WSSS) methods strive to incorporate
contextual knowledge to improve the completeness of class activation maps (CAM). In this …

Recent semi-supervised semantic segmentation methods combine pseudo labeling and
consistency regularization to enhance model generalization from perturbation-invariant …

The teacher-student framework, prevalent in semi-supervised semantic segmentation,
mainly employs the exponential moving average (EMA) to update a single teacher's weights …

This paper presents a simple but performant semi-supervised semantic segmentation
approach called CorrMatch. Previous approaches mostly employ complicated training …

Tremendous breakthroughs have been developed in Semi-Supervised Semantic
Segmentation (S4) through contrastive learning. However, due to limited annotations, the …

In semi-supervised semantic segmentation, a model is trained with a limited number of
labeled images along with a large corpus of unlabeled images to reduce the high annotation …

The critical challenge of semi-supervised semantic segmentation lies in how to fully exploit a
large volume of unlabeled data to improve the model's generalization performance for …

Semantic understanding of high-resolution remote sensing (RS) images is of great value in
Earth observation, however, it heavily depends on numerous pixel-wise manually-labeled …