Boosting Rare Scenario Perception in Autonomous Driving: An Adaptive Approach With MoEs and LoRA
Autonomous driving technology has achieved remarkable advancements, offering
substantial potential to revolutionize traffic safety and smart mobility. However, when faced …
substantial potential to revolutionize traffic safety and smart mobility. However, when faced …
MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection
Z Chen, J Meng, M Baktashmotlagh, Y Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
LiDAR-based 3D object detection is crucial for various applications but often experiences
performance degradation in real-world deployments due to domain shifts. While most …
performance degradation in real-world deployments due to domain shifts. While most …
DPO: Dual-perturbation optimization for test-time adaptation in 3d object detection
LiDAR-based 3D object detection has seen impressive advances in recent times. However,
deploying trained 3D detectors in the real world often yields unsatisfactory performance …
deploying trained 3D detectors in the real world often yields unsatisfactory performance …
DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising
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 …
samples when training the underlying detectors' deep neural networks. However, generating …
InstKD: Towards Lightweight 3D Object Detection With Instance-Aware Knowledge Distillation
H Zhang, L Liu, Y Huang, X Lei… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Deep neural network (DNN) is extensively explored for LiDAR-based 3D object detection, a
crucial perception task in the field of autonomous driving. However, the presence of …
crucial perception task in the field of autonomous driving. However, the presence of …
STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning
Existing 3D object detection suffers from expensive annotation costs and poor transferability
to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to …
to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to …