3 citations · 6 across the 7 of their papers we have counts for
7 papers
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
Xinghao Wu, Xuefeng Liu, Jianwei Niu +4
Personalized Federated Learning (PFL) is a commonly used framework that allows clients to collaboratively train their personalized models. PFL is particularly useful for handling s…
End-To-End Underwater Video Enhancement: Dataset and Model
Dazhao Du, Enhan Li, Lingyu Si +2
Underwater video enhancement (UVE) aims to improve the visibility and frame quality of underwater videos, which has significant implications for marine research and exploration. Ho…
Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects
Xiaoheng Jiang, Feng Yan, Yang Lu +6
Surface defect inspection plays an important role in the process of industrial manufacture and production. Though Convolutional Neural Network (CNN) based defect inspection methods…
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration
Xinghao Wu, Xuefeng Liu, Jianwei Niu +2
Personalized federated learning (PFL) reduces the impact of non-independent and identically distributed (non-IID) data among clients by allowing each client to train a personalized…
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space
Guogang Zhu, Xuefeng Liu, Shaojie Tang +3
Personalized federated learning (PFL) is a popular framework that allows clients to have different models to address application scenarios where clients' data are in different doma…
3Deformer: A Common Framework for Image-Guided Mesh Deformation
Hao Su, Xuefeng Liu, Jianwei Niu +2
We propose 3Deformer, a general-purpose framework for interactive 3D shape editing. Given a source 3D mesh with semantic materials, and a user-specified semantic image, 3Deformer c…