2 code implementations • ICCV 2023 • Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, Xinggang Wang
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
no code implementations • 9 Feb 2023 • Jiajie Chen
Many loss functions have been derived from cross-entropy loss functions such as large-margin softmax loss and focal loss.
no code implementations • 5 Dec 2022 • Bo Jiang, Shaoyu Chen, Xinggang Wang, Bencheng Liao, Tianheng Cheng, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving.
no code implementations • 21 Jun 2022 • Yihan Hu, Wenxin Shao, Bo Jiang, Jiajie Chen, Siqi Chai, Zhening Yang, Jingyu Qian, Helong Zhou, Qiang Liu
In this report, we introduce our solution to the Occupancy and Flow Prediction challenge in the Waymo Open Dataset Challenges at CVPR 2022, which ranks 1st on the leaderboard.
4 code implementations • 1 Feb 2021 • Helong Zhou, Liangchen Song, Jiajie Chen, Ye Zhou, Guoli Wang, Junsong Yuan, Qian Zhang
The outputs from the teacher network are used as soft labels for supervising the training of a new network.
Ranked #25 on Knowledge Distillation on ImageNet
no code implementations • ICLR 2021 • Helong Zhou, Liangchen Song, Jiajie Chen, Ye Zhou, Guoli Wang, Junsong Yuan, Qian Zhang
In this paper, we investigate the bias-variance tradeoff brought by distillation with soft labels.