Search Results for author:
Found 5 papers, 3 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
A CRF-based Framework for Tracklet Inactivation in Online Multi-Object Tracking
In this paper, a conditional random field (CRF) based framework is put forward to tackle the tracklet inactivation issue in online MOT problems.
Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation
In this work, a unified network named as FADNet is presented to address the task of monocular 3D object detection.
