Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted
unprecedented attention for its impressive zero-shot recognition ability and excellent …

Computational pathology can lead to saving human lives, but models are annotation hungry
and pathology images are notoriously expensive to annotate. Self-supervised learning has …

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in
autonomous driving to allow a vehicle to act safely in its 3D environment. The best …

Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes
continually from limited samples without forgetting the old classes. The mainstream …

For medical image segmentation, contrastive learning is the dominant practice to improve
the quality of visual representations by contrasting semantically similar and dissimilar pairs …

Recent self-supervised representation learning techniques have largely closed the gap
between supervised and unsupervised learning on ImageNet classification. While the …

In this paper, we tackle the problem of learning visual representations from unlabeled scene-
centric data. Existing works have demonstrated the potential of utilizing the underlying …

Existing semantic segmentation approaches are often limited by costly pixel-wise
annotations and predefined classes. In this work, we present CLIP-S^ 4 that leverages self …

Unsupervised semantic segmentation aims to discover groupings within and across images
that capture object-and view-invariance of a category without external supervision. Grouping …