Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

We present LSeg, a novel model for language-driven semantic image segmentation. LSeg
uses a text encoder to compute embeddings of descriptive input labels (eg," grass" or" …

Image segmentation is a significant topic in image refining and automated image analysis
with relevance for instance object recognition, diagnostic imaging scanning, mechanized …

Image segmentation is often ambiguous at the level of individual image patches and
requires contextual information to reach label consensus. In this paper we introduce …

This paper presents a new vision Transformer, called Swin Transformer, that capably serves
as a general-purpose backbone for computer vision. Challenges in adapting Transformer …

We introduce dense prediction transformers, an architecture that leverages vision
transformers in place of convolutional networks as a backbone for dense prediction tasks …

Recently, Vision Transformer and its variants have shown great promise on various
computer vision tasks. The ability of capturing short-and long-range visual dependencies …

Current semantic segmentation methods focus only on mining" local" context, ie,
dependencies between pixels within individual images, by context-aggregation modules …

Existing few-shot segmentation methods have achieved great progress based on the
support-query matching framework. But they still heavily suffer from the limited coverage of …