3 papers
cs.CV2025
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…
cs.CV2025
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…
cs.CV2025
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…