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 …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Abstract Universal Image Segmentation is not a new concept. Past attempts to unify image
segmentation include scene parsing, panoptic segmentation, and, more recently, new …

The quadratic computation complexity of self-attention has been a persistent challenge
when applying Transformer models to vision tasks. Linear attention, on the other hand, offers …

The attention module is the key component in Transformers. While the global attention
mechanism offers high expressiveness, its excessive computational cost restricts its …

Vision Transformers have been the most popular network architecture in visual recognition
recently due to the strong ability of encode global information. However, its high …

In this paper we propose the Matting Anything Model (MAM) an efficient and versatile
framework for estimating the alpha matte of any instance in an image with flexible and …

MetaFormer, the abstracted architecture of Transformer, has been found to play a significant
role in achieving competitive performance. In this paper, we further explore the capacity of …

Abstract Vision Transformer (ViT) has gained increasing attention in the computer vision
community in recent years. However the core component of ViT Self-Attention lacks explicit …

Self-attention mechanism has been a key factor in the recent progress of Vision Transformer
(ViT), which enables adaptive feature extraction from global contexts. However, existing self …