49 citations · 80 across the 6 of their papers we have counts for
6 papers
Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation
Shaolei Liu, Siqi Yin, Linhao Qu +1
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing…
Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)
Kexue Fu, Jiazheng Luo, Xiaoyuan Luo +3
3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, t…
POS-BERT: Point Cloud One-Stage BERT Pre-Training
Kexue Fu, Peng Gao, ShaoLei Liu +3
Recently, the pre-training paradigm combining Transformer and masked language modeling has achieved tremendous success in NLP, images, and point clouds, such as BERT. However, dire…
TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning
Linhao Qu, Shaolei Liu, Manning Wang +4
Image fusion is a technique to integrate information from multiple source images with complementary information to improve the richness of a single image. Due to insufficient task-…
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds
Xiaoyuan Luo, Shaolei Liu, Kexue Fu +2
Deep neural networks have achieved promising performance in supervised point cloud applications, but manual annotation is extremely expensive and time-consuming in supervised learn…
Robust Point Cloud Registration Framework Based on Deep Graph Matching
Kexue Fu, Shaolei Liu, Xiaoyuan Luo +1
3D point cloud registration is a fundamental problem in computer vision and robotics. There has been extensive research in this area, but existing methods meet great challenges in…