Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

Mainstream 3D representation learning approaches are built upon contrastive or generative
modeling pretext tasks, where great improvements in performance on various downstream …

We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence
embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference …

Leading graph contrastive learning (GCL) methods perform graph augmentations in two
fashions:(1) randomly corrupting the anchor graph, which could cause the loss of semantic …

Prevalent state-of-the-art instance segmentation methods fall into a query-based scheme, in
which instance masks are derived by querying the image feature using a set of instance …

This study explores the concept of equivariance in vision-language foundation models
(VLMs), focusing specifically on the multimodal similarity function that is not only the major …

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …

Abstract Recently, Masked Image Modeling (MIM) achieves great success in self-supervised
visual recognition. However, as a reconstruction-based framework, it is still an open …

Abstract Weakly Supervised Semantic Segmentation (WSSS) research has explored many
directions to improve the typical pipeline CNN plus class activation maps (CAM) plus …

In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …