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 …

Training semantic segmentation models with few annotated samples has great potential in
various real-world applications. For the few-shot segmentation task, the main challenge is …

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 …

A major barrier to applying deep segmentation models in the medical domain is their typical
data-hungry nature, requiring experts to collect and label large amounts of data for training …

Affordance detection refers to identifying the potential action possibilities of objects in an
image, which is a crucial ability for robot perception and manipulation. To empower robots …

Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of unseen
classes by resorting to a small set of support images that contain pixel-level object …

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 …

Significant strides have been made using large vision-language models, like Stable
Diffusion (SD), for a variety of downstream tasks, including image editing, image …

Few-shot segmentation (FSS) aims to segment novel categories given scarce annotated
support images. The crux of FSS is how to aggregate dense correlations between support …

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 …