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 …

The central challenge in few-shot learning involves (1) acquiring object proposals through
the support representation,(2) ensuring consistent representations for images in both …

Contemporary cross-domain remote sensing (RS) image segmentation has been successful
in recent years. When the target domain data becomes scarce in some realistic scenarios …

Real-time segmentation plays an important role in numerous applications, including
autonomous driving and medical imaging, where accurate and instantaneous segmentation …

The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain
disparity between the training and inference phases, which can exist in either the input data …

Medical images often exhibit distribution shifts due to variations in imaging protocols and
scanners across different medical centers. Domain Generalization (DG) methods aim to train …

Segment Anything Model (SAM) represents a large-scale segmentation model that enables
powerful zero-shot capabilities with flexible prompts. While SAM can segment any object in …

Under the backdrop of large-scale pre-training, large visual models (LVM) have
demonstrated significant potential in image understanding. The recent emergence of the …

Cross-domain few-shot segmentation (CD-FSS) aims to segment objects of novel classes
under domain shifts, using only a few mask-annotated support samples. However, directly …

Abstract The potential of Open-Vocabulary Semantic Segmentation (OVSS) in few-shot
scenarios is not fully explored due to the complexity of extending few-shot concepts to …