Search Results for author:
Found 3 papers, 2 papers with code
Minimalist and High-Performance Semantic Segmentation with Plain Vision Transformers
Building upon the original motivations of plain ViTs, which are simplicity and generality, we explore high-performance `minimalist' systems to this end.
Ranked #1 on
Semantic Segmentation
on PASCAL Context
Representation Separation for Semantic Segmentation with Vision Transformers
Vision transformers (ViTs) encoding an image as a sequence of patches bring new paradigms for semantic segmentation. We present an efficient framework of representation separation in local-patch level and global-region level for semantic segmentation with ViTs.
Ranked #2 on
Semantic Segmentation
on COCO-Stuff test
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
The proposed deep dual-resolution networks (DDRNets) are composed of two deep branches between which multiple bilateral fusions are performed.
Ranked #2 on
All-day Semantic Segmentation
on All-day CityScapes
