The booming of 3D recognition in the 2020s began with the introduction of point cloud
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …

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 …

Few-shot segmentation (FSS) aims to segment objects of new categories given only a
handful of annotated samples. Previous works focus their efforts on exploring the support …

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 …

Diffusion models have demonstrated excellent performance in image generation. Although
various few-shot semantic segmentation (FSS) models with different network structures have …

In this work, we revisit the prior mask guidance proposed in “Prior Guided Feature
Enrichment Network for Few-Shot Segmentation”. The prior mask serves as an indicator that …

This study addresses the Domain-Class Incremental Learning problem, a realistic but
challenging continual learning scenario where both the domain distribution and target …

Continual learning has gained increasing importance as it facilitates the acquisition and
refinement of scalable knowledge and skills in language models. However, existing …

Vision-language pre-training (VLP) aims to learn joint representations of vision and
language modalities. The contrastive paradigm is currently dominant in this field. However …

Plant segmentation is a challenging computer vision task due to plant images complexity.
For many practical problems, we have to solve even more difficult tasks. We need to …