33 citations · 38 across the 10 of their papers we have counts for
10 papers
Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation
Wangkai Li, Zhaoyang Li, Yuwen Pan +3
Adverse weather conditions significantly degrade the performance of LiDAR point cloud semantic segmentation networks by introducing large distribution shifts. Existing augmentation…
DA-Cal: Towards Cross-Domain Calibration in Semantic Segmentation
Wangkai Li, Rui Sun, Zhaoyang Li +2
While existing unsupervised domain adaptation (UDA) methods greatly enhance target domain performance in semantic segmentation, they often neglect network calibration quality, resu…
Towards Robust Pseudo-Label Learning in Semantic Segmentation: An Encoding Perspective
Wangkai Li, Rui Sun, Zhaoyang Li +1
Pseudo-label learning is widely used in semantic segmentation, particularly in label-scarce scenarios such as unsupervised domain adaptation (UDA) and semisupervised learning (SSL)…
Balanced Learning for Domain Adaptive Semantic Segmentation
Wangkai Li, Rui Sun, Bohao Liao +2
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Despite the effectiveness of s…
Towards Unsupervised Domain Bridging via Image Degradation in Semantic Segmentation
Wangkai Li, Rui Sun, Huayu Mai +1
Semantic segmentation suffers from significant performance degradation when the trained network is applied to a different domain. To address this issue, unsupervised domain adaptat…
Exploring Reliable Matching with Phase Enhancement for Night-time Semantic Segmentation
Yuwen Pan, Rui Sun, Naisong Luo +2
Semantic segmentation of night-time images holds significant importance in computer vision, particularly for applications like night environment perception in autonomous driving sy…
Localization and Expansion: A Decoupled Framework for Point Cloud Few-shot Semantic Segmentation
Zhaoyang Li, Yuan Wang, Wangkai Li +2
Point cloud few-shot semantic segmentation (PC-FSS) aims to segment targets of novel categories in a given query point cloud with only a few annotated support samples. The current…
Image-to-Image Matching via Foundation Models: A New Perspective for Open-Vocabulary Semantic Segmentation
Yuan Wang, Rui Sun, Naisong Luo +2
Open-vocabulary semantic segmentation (OVS) aims to segment images of arbitrary categories specified by class labels or captions. However, most previous best-performing methods, wh…
TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation
Rui Sun, Tao Lei, Weichuan Zhang +3
The hybrid architecture of convolution neural networks (CNN) and Transformer has been the most popular method for medical image segmentation. However, the existing networks based o…
CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation
Tao Lei, Rui Sun, Xuan Wang +3
The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation. However, it suffers from two challenges. First, alt…