Author

Q. M. Jonathan Wu

2 papers indexed here

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Preprint Oct 2026

FogTTA: Online Test-Time Adaptation for Robust Transformer-based Object Detection in Foggy Weather

Object detection models for autonomous driving commonly experience substantial performance drops when deployed under adverse weather due to the domain shift between training data and real-world operating conditions. This degradation is especially evident when models trained on clear-weather images encounter foggy envir...

Ali Abedi, Q. M. Jonathan Wu, Ning Zhang et al. · 0 citations

Open access Jul 2024

Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

Efficient Unsupervised Domain Adaptation (EUDA) is proposed, a parameter-efficient framework that leverages a frozen DINOv2 backbone as a feature extractor and updates only a lightweight bottleneck and classification head to promote both discriminative learning and cross-domain alignment.

Ali Abedi, Q. M. Jonathan Wu, Ning Zhang et al. · International Journal of Mac... · 9 citations