Artificial intelligence (AI) is evolving towards artificial general intelligence, which refers to the
ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence …

This work aims to leverage pre-trained foundation models, such as contrastive language-
image pre-training (CLIP) and segment anything model (SAM), to address weakly …

In this work, we propose a new transformer-based regularization to better localize objects for
Weakly supervised semantic segmentation (WSSS). In image-level WSSS, Class Activation …

Weakly-supervised visual recognition using inexact supervision is a critical yet challenging
learning problem. It significantly reduces human labeling costs and traditionally relies on …

Crop mapping using satellite imagery is crucial for agriculture applications. However, a
fundamental challenge that hinders crop mapping progress is the scarcity of samples. The …

Segment anything model (SAM) has emerged as the leading approach for zero-shot
learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It …

Building extraction from remote sensing images is a hot topic in the fields of computer vision
and remote sensing. In recent years, driven by deep learning, the accuracy of building …

Contrastive language and image pre-training (CLIP) achieves great success in various
computer vision tasks and also presents an opportune avenue for enhancing weakly …

Segmenting and recognizing diverse object parts is crucial in computer vision and robotics.
Despite significant progress in object segmentation, part-level segmentation remains …

The advance of generative models for images has inspired various training techniques for
image recognition utilizing synthetic images. In semantic segmentation, one promising …