1 code implementation • 30 Mar 2024 • Tao Li, Qinghua Tao, Weihao Yan, Zehao Lei, Yingwen Wu, Kun Fang, Mingzhen He, Xiaolin Huang
Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning.
1 code implementation • 22 Nov 2023 • Weihao Yan, Yeqiang Qian, Xingyuan Chen, Hanyang Zhuang, Chunxiang Wang, Ming Yang
It involves Semantic-Guided Mask Labeling, which assigns semantic labels to unlabeled SAM masks using UDA pseudo-labels.
no code implementations • 18 Nov 2023 • Yueyuan Li, Wei Yuan, Songan Zhang, Weihao Yan, Qiyuan Shen, Chunxiang Wang, Ming Yang
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings.
1 code implementation • 21 Nov 2022 • Tao Li, Weihao Yan, Zehao Lei, Yingwen Wu, Kun Fang, Ming Yang, Xiaolin Huang
To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability.
no code implementations • 7 Sep 2022 • Weihao Yan, Yeqiang Qian, Chunxiang Wang, Ming Yang
Panoptic segmentation combines the advantages of semantic and instance segmentation, which can provide both pixel-level and instance-level environmental perception information for intelligent vehicles.
1 code implementation • 23 Aug 2022 • Weihao Yan, Yeqiang Qian, Chunxiang Wang, Ming Yang
In stage one, we design a threshold-adaptative unsupervised focal loss to regularize the prediction in the target domain, which has a mild gradient neutralization mechanism and mitigates the problem that hard samples are barely optimized in entropy-based methods.