4 citations · 4 across the 5 of their papers we have counts for
5 papers
MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving
Xiaohu Lu, Hamed Khatounabadi, Hayder Radha
With the advancement of autonomous driving, numerous annotated multi-modality datasets have become available. This presents an opportunity to develop domain-adaptive 3D object dete…
WILD SAM: A Simulated-and-Real Data Augmentation for Autonomous Driving Perception under Challenging Weather
Hamed Khatounabadi, Xiaohu Lu, Hayder Radha
The performance of state-of-the-art object detectors degrades significantly under adverse weather, causing a safety-critical domain shift problem for autonomous vehicles. Recent ef…
DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising
Xiaohu Lu, Hayder Radha
Object detection using LiDAR point clouds relies on a large amount of human-annotated samples when training the underlying detectors' deep neural networks. However, generating 3D b…
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
Xiaohu Lu, Hayder Radha
The adversarial robustness of a model is its ability to resist adversarial attacks in the form of small perturbations to input data. Universal adversarial attack methods such as Fa…
Strong-Weak Integrated Semi-supervision for Unsupervised Single and Multi Target Domain Adaptation
Xiaohu Lu, Hayder Radha
Unsupervised domain adaptation (UDA) focuses on transferring knowledge learned in the labeled source domain to the unlabeled target domain. Despite significant progress that has be…