In this paper, we address the challenging source-free unsupervised domain adaptation
(SFUDA) for pinhole-to-panoramic semantic segmentation, given only a pinhole image pre …

Image segmentation is a long-standing challenge in computer vision, studied continuously
over several decades, as evidenced by seminal algorithms such as N-Cut, FCN, and …

Abstract The Segmentation Anything Model (SAM) requires labor-intensive data labeling.
We present Unsupervised SAM (UnSAM) for promptable and automatic wholeimage …

Contemporary cutting-edge open-vocabulary segmentation approaches commonly rely on
image-mask-text triplets, yet this restricted annotation is labour-intensive and encounters …

Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in
processing vision-language tasks. One of the crux of MLLMs lies in vision tokenization …

Vision-language models (VLMs) have demonstrated remarkable open-vocabulary object
recognition capabilities, motivating their adaptation for dense prediction tasks like …

In the past few years, we have observed rapid growth in digital content. Even in the
biological domain, the arrival of microscopic and nanoscopic images and videos captured …

Dealing with atypical traffic scenarios remains a challenging task in autonomous driving.
However, most anomaly detection approaches cannot be trained on raw sensor data but …

Segmenting objects from cluttered backgrounds in single-channel images, such as marine
radar echoes, medical images, and remote sensing images, poses significant challenges …

Introduction Ultrasound imaging has become a crucial tool in medical diagnostics, offering
real-time visualization of internal organs and tissues. However, challenges such as low …