Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary substantially due to [...]
Benchmarking the Robustness of LiDAR Semantic Segmentation Models Xu Yan, Chaoda Zheng, Zhen Li, Shuguang Cui, Dengxin Dai When using LiDAR semantic segmentation models for safety-critical applications [...]
Learnable Online Graph Representations for 3D Multi-Object Tracking JN Zaech, D Dai, A Liniger, M Danelljan, L Van Gool Autonomous systems that operate [...]
Pix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc Van Gool We propose [...]
Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning Yue Fan, Dengxin Dai, Anna Kukleva, Bernt Schiele Standard semi-supervised learning (SSL) using class-balanced [...]
End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization Niclas Vödisch, Ozan Unal, Ke Li, Luc Van Gool, and Dengxin Dai [...]
Adiabatic Quantum Computing for Multi Object Tracking Jan-Nico Zaech, Alexander Liniger, Martin Danelljan, Dengxin Dai, Luc Van Gool Multi-Object Tracking (MOT) is most [...]
LiDAR Snowfall Simulation for Robust 3D Object Detection Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, Luc Van Gool [...]
Continual Test-Time Domain Adaptation Qin Wang, Olga Fink, Luc Van Gool, Dengxin Dai Test-time domain adaptation aims to adapt a source pre-trained model [...]
Decoupling Zero-Shot Semantic Segmentation Jian Ding, Nan Xue, Gui-Song Xia and Dengxin Dai Zero-shot semantic segmentation (ZS3) aims to segment the novel categories [...]
