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 propose a simple, efficient, yet powerful framework for dense visual predictions based
on the conditional diffusion pipeline. Our approach follows a" noise-to-map" generative …

In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo)
which works well on both imbalanced and balanced data. Based on theoretical analysis, we …

Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …

Most of the existing salient object detection (SOD) models focus on improving the overall
model performance, without explicitly explaining the discrepancy between the training and …

Semantic segmentation is still a challenging task for parsing diverse contexts in different
scenes, thus the fixed classifier might not be able to well address varying feature …

Abstract Panoptic Scene Graph generation (PSG) is a recently proposed task in image
scene understanding that aims to segment the image and extract triplets of subjects, objects …

Vision transformers (ViTs) encoding an image as a sequence of patches bring new
paradigms for semantic segmentation. We present an efficient framework of representation …

Medical image segmentation can effectively identify lesions in medicine, but some small and
rare lesions cannot be well identified. Existing studies do not take into account the …

The successful application of semantic segmentation technology in the real world has been
among the most exciting achievements in the computer vision community over the past …