2 citations · 3 across the 6 of their papers we have counts for
7 papers
One-shot federated unsupervised domain adaptation with Smoothed Knowledge Distillation and teacher refinement
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Federated unsupervised domain adaptation (FUDA) addresses the challenge of adapting models to an unlabeled target domain using decentralized source domains while preserving data pr…
Compressing Multi-Task Model for Autonomous Driving via Pruning and Knowledge Distillation
Jiayuan Wang, Q. M. Jonathan Wu, Ning Zhang +2
Autonomous driving systems rely on panoptic perception to jointly handle object detection, drivable area segmentation, and lane line segmentation. Although multi-task learning is a…
RMT-PPAD: Real-time Multi-task Learning for Panoptic Perception in Autonomous Driving
Jiayuan Wang, Q. M. Jonathan Wu, Katsuya Suto +1
Autonomous driving systems rely on panoptic driving perception that requires both precision and real-time performance. In this work, we propose RMT-PPAD, a real-time, transformer-b…
A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles
Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu +1
Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in co…
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Federated Learning (FL) is a promising approach for privacy-preserving collaborative learning. However, it faces significant challenges when dealing with domain shifts, especially…
Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data. Despite recent advance…
You Only Look at Once for Real-time and Generic Multi-Task
Jiayuan Wang, Q. M. Jonathan Wu, Ning Zhang
High precision, lightweight, and real-time responsiveness are three essential requirements for implementing autonomous driving. In this study, we incorporate A-YOLOM, an adaptive,…