26 citations · 46 across the 5 of their papers we have counts for
5 papers
Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data
Gang Fu, Qing Zhang, Lei Zhu +2
This paper aims to remove specular highlights from a single object-level image. Although previous methods have made some progresses, their performance remains somewhat limited, par…
Rethinking Client Drift in Federated Learning: A Logit Perspective
Yunlu Yan, Chun-Mei Feng, Mang Ye +5
Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. However, the real-world non-IID data will lead to c…
Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19
Liansheng Wang, Jiacheng Wang, Lei Zhu +6
Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19. However, there are still some challenges for developing AI sy…
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
Xiaoxiao Liang, Yiqun Lin, Huazhu Fu +2
Federated semi-supervised learning (FSSL) aims to derive a global model by training fully-labeled and fully-unlabeled clients or training partially labeled clients. The existing ap…
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
Xiaoqiang Wang, Lei Zhu, Siliang Tang +5
Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the de…