Despite the progress made by few-shot segmentation (FSS) in low-data regimes, the
generalization capability of most previous works could be fragile when countering hard …

Semantic segmentation assigns category labels to each pixel in an image, enabling
breakthroughs in fields such as autonomous driving and robotics. Deep Neural Networks …

Few-shot semantic segmentation is the task of learning to locate each pixel of the novel
class in the query image with only a few annotated support images. The current correlation …

We address the task of weakly-supervised few-shot image classification and segmentation,
by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed …

Few-shot semantic segmentation (FSS) is a challenging task that aims to learn to segment
novel categories with only a few labeled images, and it has a wide range of real-world …

Abstract We present Lazy Visual Grounding for open-vocabulary semantic segmentation,
which decouples unsupervised object mask discovery from object grounding. Plenty of the …

FSS (Few-shot segmentation) aims to segment a target class using a small number of
labeled images (support set). To extract the information relevant to target class, a dominant …

Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is
troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based …

Abstract Few-Shot Segmentation (FSS) aims to segment the novel class images with a few
annotated samples. In the past, numerous studies have concentrated on cross-category …

Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes
with limited support examples. Existing methods based on Region Proposal Networks …