3 papers
cs.CV2024
TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
Junbao Zhou, Jilin Mei, Pengze Wu +4
In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle's surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem…
cs.RO2023
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving
Jilin Mei, Junbao Zhou, Yu Hu
In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible wa…
cs.CV2023
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Shun Lu, Yu Hu, Longxing Yang +4
Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. Howe…