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 (Radford et al., 2021) aims to match the
paired image and caption embeddings while pushing others apart, which improves …

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

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

Self-supervised audio-visual source localization aims to locate sound-source objects in
video frames without extra annotations. Recent methods often approach this goal with the …

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 …

Existing deep clustering methods rely on either contrastive or non-contrastive representation
learning for downstream clustering task. Contrastive-based methods thanks to negative …