Revisiting weak-to-strong consistency in semi-supervised semantic segmentation
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 …
from semi-supervised classification, where the prediction of a weakly perturbed image …
Enhanced soft label for semi-supervised semantic segmentation
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 …
pseudo labeling and its variants have witnessed impressive progress in semi-supervised …
Semi-supervised semantic segmentation with prototype-based consistency regularization
Semi-supervised semantic segmentation requires the model to effectively propagate the
label information from limited annotated images to unlabeled ones. A challenge for such a …
label information from limited annotated images to unlabeled ones. A challenge for such a …
Conflict-based cross-view consistency for semi-supervised semantic segmentation
Semi-supervised semantic segmentation (SSS) has recently gained increasing research
interest as it can reduce the requirement for large-scale fully-annotated training data. The …
interest as it can reduce the requirement for large-scale fully-annotated training data. The …
Hunting sparsity: Density-guided contrastive learning for semi-supervised semantic segmentation
Recent semi-supervised semantic segmentation methods combine pseudo labeling and
consistency regularization to enhance model generalization from perturbation-invariant …
consistency regularization to enhance model generalization from perturbation-invariant …
SemiVL: semi-supervised semantic segmentation with vision-language guidance
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 …
labeled images along with a large corpus of unlabeled images to reduce the high annotation …
Learning from future: A novel self-training framework for semantic segmentation
Self-training has shown great potential in semi-supervised learning. Its core idea is to use
the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in …
the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in …
Virtual category learning: A semi-supervised learning method for dense prediction with extremely limited labels
Due to the costliness of labelled data in real-world applications, semi-supervised learning,
underpinned by pseudo labelling, is an appealing solution. However, handling confusing …
underpinned by pseudo labelling, is an appealing solution. However, handling confusing …
Space engage: Collaborative space supervision for contrastive-based semi-supervised semantic segmentation
Abstract Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model
with limited labeled images and a substantial volume of unlabeled images. To improve the …
with limited labeled images and a substantial volume of unlabeled images. To improve the …
Pseudo Labeling Methods for Semi-Supervised Semantic Segmentation: A Review and Future Perspectives
Semantic segmentation is a fundamental task in computer vision and finds extensive
applications in scene understanding, medical image analysis, and remote sensing. With the …
applications in scene understanding, medical image analysis, and remote sensing. With the …