20 citations · 83 across the 20 of their papers we have counts for
53 papers
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
Ozan Unal, Dengxin Dai, Lukas Hoyer +2
As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necess…
Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts
Qi Fan, Mattia Segu, Yu-Wing Tai +4
Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary subst…
MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction
Shaoshuai Shi, Li Jiang, Dengxin Dai +1
In this report, we present the 1st place solution for motion prediction track in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer framework for multimodal…
Bi-level Alignment for Cross-Domain Crowd Counting
Shenjian Gong, Shanshan Zhang, Jian Yang +2
Recently, crowd density estimation has received increasing attention. The main challenge for this task is to achieve high-quality manual annotations on a large amount of training d…
Scribble-Supervised LiDAR Semantic Segmentation
Ozan Unal, Dengxin Dai, Luc Van Gool
Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data. While current literature focuses on fully-supervised…
Continual Test-Time Domain Adaptation
Qin Wang, Olga Fink, Luc Van Gool +1
Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target doma…