Although deep learning algorithms have achieved significant progress in a variety of
domains, they require costly annotations on huge datasets. Self-supervised learning (SSL) …

Existing vision-text contrastive learning like CLIP aims to match the paired image and
caption embeddings while pushing others apart, which improves representation …

Learning with few labeled data has been a longstanding problem in the computer vision and
machine learning research community. In this paper, we introduced a new semi-supervised …

Benefiting from the large-scale archiving of digitized whole-slide images (WSIs), computer-
aided diagnosis has been well developed to assist pathologists in decision-making. Content …

Generalized category discovery (GCD) is a recently proposed open-world problem, which
aims to automatically cluster partially labeled data. The main challenge is that the unlabeled …

Although an object may appear in numerous contexts, we often describe it in a limited
number of ways. Language allows us to abstract away visual variation to represent and …

Although recent works in semi-supervised learning (SemiSL) have accomplished significant
success in natural image segmentation, the task of learning discriminative representations …

Although existing semi-supervised learning models achieve remarkable success in learning
with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled …

We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision
Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate …

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved
great success in learning visual representations without data annotations. However, most of …