4 papers
FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
Liu junkang, Yuanyuan Liu, Fanhua Shang +3
For federated learning (FL) algorithms such as FedSAM, their generalization capability is crucial for real-word applications. In this paper, we revisit the generalization problem i…
NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation
Xuzhi Wang, Wei Feng, Lingdong Kong +1
LiDAR semantic segmentation plays a vital role in autonomous driving. Existing voxel-based methods for LiDAR semantic segmentation apply uniform partition to the 3D LiDAR point clo…
Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation
Hongmei Yin, Tingliang Feng, Fan Lyu +4
In this work, we focus on continual semantic segmentation (CSS), where segmentation networks are required to continuously learn new classes without erasing knowledge of previously…
Casual Inference via Style Bias Deconfounding for Domain Generalization
Jiaxi Li, Di Lin, Hao Chen +3
Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in diverse realworld applications. To address this issue, domain generalization…