19 citations · 24 across the 19 of their papers we have counts for
19 papers
Route by Kinematics, Act by Observation: Kinematics-Supervised Expert Routing in MoE-Augmented VLA
Tianhang Yang, Yanze Zheng, Junjie Wang +3
While MoE augments VLA via expert specialization, router suffers from ineffective expert routing owing to the kinematic heterogeneity of actions across manipulation tasks and, even…
AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligence
Wei-Bin Kou, Jingreng Lei, Guangxu Zhu +1
Mixture of Experts (MoE) are increasingly deployed over wireless cloud-edge networks, as a single edge device lacks sufficient resources to host large-scale models locally. In this…
OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
Wei-Bin Kou, Guangxu Zhu, Ming Tang +4
The global deployment of edge intelligence operates across heterogeneous legal frameworks. While some regions permit centralized learning (CL) via cloud data aggregation, others en…
Integrated and Cross-Architecture Interpretation of LLM Reasoning
Leonardo Matthew Yauw, Wei-Bin Kou, Yujiu Yang
Understanding how LLMs reason is hindered by a practical asymmetry: while their generated outputs are observable, the underlying reasoning patterns remain opaque. Relying on single…
FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng +3
Federated Learning (FL) enables collaborative training of autonomous driving (AD) models across distributed vehicles while preserving data privacy. However, FL encounters critical…
Statistic-Augmented, Decoupled MoE Routing and Aggregating in Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Jingreng Lei +3
Autonomous driving (AD) scenarios are inherently complex and diverse, posing significant challenges for a single deep learning model to effectively cover all possible conditions, s…
NTK-Guided Implicit Neural Teaching
Chen Zhang, Wei Zuo, Bingyang Cheng +4
Implicit Neural Representations (INRs) parameterize continuous signals via multilayer perceptrons (MLPs), enabling compact, resolution-independent modeling for tasks like image, au…
Adverse Weather-Independent Framework Towards Autonomous Driving Perception through Temporal Correlation and Unfolded Regularization
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +5
Various adverse weather conditions such as fog and rain pose a significant challenge to autonomous driving (AD) perception tasks like semantic segmentation, object detection, etc.…
iMacHSR: Intermediate Multi-Access Heterogeneous Supervision and Regularization Scheme Toward Architecture-Agnostic Training
Wei-Bin Kou, Guangxu Zhu, Yichen Jin +4
While deep supervision is a powerful training strategy by supervising intermediate layers with auxiliary losses, it faces three underexplored problems: (I) Existing deep supervisio…
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng +3
Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization d…
Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +3
Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the q…
Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory
Wei-Bin Kou, Qingfeng Lin, Ming Tang +5
To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progres…
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving
Wei-Bin Kou, Qingfeng Lin, Ming Tang +4
Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous driving (AD). However, inference model trained from data in a particular geographical region…
Generalizable Autonomous Driving System across Diverse Adverse Weather Conditions
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +4
Various adverse weather conditions pose a significant challenge to autonomous driving (AD) street scene semantic understanding (segmentation). A common strategy is to minimize the…
Pareto Front Shape-Agnostic Pareto Set Learning in Multi-Objective Optimization
Rongguang Ye, Longcan Chen, Wei-Bin Kou +2
Pareto set learning (PSL) is an emerging approach for acquiring the complete Pareto set of a multi-objective optimization problem. Existing methods primarily rely on the mapping of…
FedRC: A Rapid-Converged Hierarchical Federated Learning Framework in Street Scene Semantic Understanding
Wei-Bin Kou, Qingfeng Lin, Ming Tang +3
Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model…
pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
Wei-Bin Kou, Qingfeng Lin, Ming Tang +8
Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL)…
PraFFL: A Preference-Aware Scheme in Fair Federated Learning
Rongguang Ye, Wei-Bin Kou, Ming Tang
Fairness in federated learning has emerged as a critical concern, aiming to develop an unbiased model among groups (e.g., male or female) of diverse sensitive features. However, th…
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
Wei-Bin Kou, Shuai Wang, Guangxu Zhu +4
While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence…