Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

We present SegGPT, a generalist model for segmenting everything in context. We unify
various segmentation tasks into a generalist in-context learning framework that …

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting
unseen classes with only a handful of annotations. Previous methods limited to the semantic …

We present SegGPT, a generalist model for segmenting everything in context. We unify
various segmentation tasks into a generalist in-context learning framework that …

In this paper we propose a novel Visual Reference Prompt (VRP) encoder that empowers
the Segment Anything Model (SAM) to utilize annotated reference images as prompts for …

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 …

Existing few-shot segmentation methods are based on the meta-learning strategy and
extract instance knowledge from a support set and then apply the knowledge to segment …

The key to the success of few-shot segmentation (FSS) lies in how to effectively utilize
support samples. Most solutions compress support foreground (FG) features into prototypes …

This paper proposes LLaFS the first attempt to leverage large language models (LLMs) in
few-shot segmentation. In contrast to the conventional few-shot segmentation methods that …

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 …