Zobrazeno 1 - 10
of 233
pro vyhledávání: '"Zhou, Changqing"'
Autor:
Li, Shiyi, Zhou, Changqing, Xu, Yongqian, Wang, Yujia, Li, Lijiao, Pelekos, George, Ziebolz, Dirk, Schmalz, Gerhard, Qin, Zeman
Background: This bioinformatics study aimed to reveal potential cross-talk genes, related pathways, and transcription factors between periimplantitis and rheumatoid arthritis (RA). Methods: The datasets GSE33774 (seven periimplantitis and eight contr
Externí odkaz:
https://ul.qucosa.de/id/qucosa%3A84303
https://ul.qucosa.de/api/qucosa%3A84303/attachment/ATT-0/
https://ul.qucosa.de/api/qucosa%3A84303/attachment/ATT-0/
Autor:
Luo, Zhipeng, Zhang, Gongjie, Zhou, Changqing, Wu, Zhonghua, Tao, Qingyi, Lu, Lewei, Lu, Shijian
The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics. However, existing approaches have primarily relied on appearance matching or motion modeling within only
Externí odkaz:
http://arxiv.org/abs/2303.07605
3D object detection with surround-view images is an essential task for autonomous driving. In this work, we propose DETR4D, a Transformer-based framework that explores sparse attention and direct feature query for 3D object detection in multi-view im
Externí odkaz:
http://arxiv.org/abs/2212.07849
Autor:
Luo, Zhipeng, Zhou, Changqing, Pan, Liang, Zhang, Gongjie, Liu, Tianrui, Luo, Yueru, Zhao, Haiyu, Liu, Ziwei, Lu, Shijian
With the prevalence of LiDAR sensors in autonomous driving, 3D object tracking has received increasing attention. In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames given an o
Externí odkaz:
http://arxiv.org/abs/2208.05216
3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics. However, most existing studies focus on single point cloud frames without harnessing the temporal information i
Externí odkaz:
http://arxiv.org/abs/2208.03141
Autor:
Zhou, Changqing, Luo, Zhipeng, Luo, Yueru, Liu, Tianrui, Pan, Liang, Cai, Zhongang, Zhao, Haiyu, Lu, Shijian
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by the success of transformers, we propose Point Tracking TRansformer (P
Externí odkaz:
http://arxiv.org/abs/2112.02857
Autor:
Luo, Zhipeng, Cai, Zhongang, Zhou, Changqing, Zhang, Gongjie, Zhao, Haiyu, Yi, Shuai, Lu, Shijian, Li, Hongsheng, Zhang, Shanghang, Liu, Ziwei
Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. However, drastic performance degradation remains a critical challenge for cross-domain deployment. In addition, exi
Externí odkaz:
http://arxiv.org/abs/2107.11355
Adversarial training (AT) is one of the most effective ways for improving the robustness of deep convolution neural networks (CNNs). Just like common network training, the effectiveness of AT relies on the design of basic network components. In this
Externí odkaz:
http://arxiv.org/abs/2105.08269
Autor:
Li, Jun, Hong, Yinghui, Zhong, Yinsheng, Yang, Shujun, Pei, Liying, Huang, Zijie, Long, Huibao, Chen, Xuxiang, Zhou, Changqing, Zheng, Guanghui, Zeng, Chaotao, Wu, Haidong, Wang, Tong
Publikováno v:
In BBA - Molecular Basis of Disease April 2024 1870(4)
Autor:
Fu, Lan, Zhou, Changqing, Guo, Qing, Juefei-Xu, Felix, Yu, Hongkai, Feng, Wei, Liu, Yang, Wang, Song
Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful state-of-the-art deep neural networks could hardly recover traceless sha
Externí odkaz:
http://arxiv.org/abs/2103.01255