Volume-wise labeling in 3D medical images is a time-consuming task that requires
expertise. As a result, there is growing interest in using semi-supervised learning (SSL) …

Vision-language models (VLMs) that use contrastive language-image pre-training have
shown promising zero-shot classification performance. However, their performance on …

We address the challenging problem of Long-Tailed Semi-Supervised Learning (LTSSL)
where labeled data exhibit imbalanced class distribution and unlabeled data follow an …

The volume-wise labeling of 3D medical images is expertise-demanded and time-
consuming; hence semi-supervised learning (SSL) is highly desirable for training with …

Class imbalance poses a significant challenge in semi-supervised medical image
segmentation (SSLMIS). Existing techniques face problems such as poor performance on …

Abstract Wafer Bin Map (WBM) defect patterns are a critical aspect of identifying the root
cause of manufacturing defects in the semiconductor industry. Semi-supervised learning …

Semi-supervised learning (SSL) algorithms struggle to perform well when exposed to
imbalanced training data. In this scenario, the generated pseudo-labels can exhibit a bias …

Pseudo-labeling and consistency-based co-training are established paradigms in semi-
supervised learning. Pseudo-labeling focuses on selecting reliable pseudo-labels, while co …

In this work, we address the real-world, challenging task of out-of-context misinformation
detection, where a real image is paired with an incorrect caption for creating fake news …

Annotating 3D medical images demands substantial time and expertise, driving the adoption
of semi-supervised learning (SSL) for segmentation tasks. However, the complex anatomical …