2 citations · 3 across the 11 of their papers we have counts for
12 papers
Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation
Chengyang Li, Yujie Wan, Shuai Wang +6
This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories are aggregated at a ground server to answer questions about o…
Memory-Native Non-Terrestrial Networks for Embodied Intelligence
Chengyang Li, Yikun Wang, Jiahui He +6
Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in the wilderness to leverage cloud resources or report critical info…
Memory Centric Power Allocation for Multi-Agent Embodied Question Answering
Chengyang Li, Shuai Wang, Kejiang Ye +5
This paper considers multi-agent embodied question answering (MA-EQA), which enables robot teams to answer queries based on their long-horizon observations. In contrast to existing…
LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
Yikun Wang, Yujie Wan, Wei Zuo +4
Low-altitude Gaussian splatting (LAGS) facilitates 3D scene reconstruction by aggregating aerial images from distributed drones. However, as LAGS prioritizes maximizing reconstruct…
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…
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)…