1 code implementation • 25 Mar 2024 • Xiaoxuan Yu, Hao Wang, Weiming Li, Qiang Wang, SoonYong Cho, Younghun Sung
In this work, we propose a novel Disentangled Object-Centric TRansformer (DOCTR) that explores object-centric representation to facilitate learning with multiple objects for the multiple sub-tasks in a unified manner.
no code implementations • 20 Mar 2024 • Yamin Mao, Zhihua Liu, Weiming Li, SoonYong Cho, Qiang Wang, Xiaoshuai Hao
Recently, dense regression methods have attracted increasing attention in 3D hand pose estimation task, which provide a low computational burden and high accuracy regression way by densely regressing hand joint offset maps.
no code implementations • 25 Sep 2023 • Xiongfeng Peng, Zhihua Liu, Weiming Li, Ping Tan, SoonYong Cho, Qiang Wang
Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress.
no code implementations • 30 Mar 2023 • Weiming Li, Xueqian Wang, Gang Li
Change detection (CD) in heterogeneous remote sensing images is a practical and challenging issue for real-life emergencies.
no code implementations • 14 Oct 2022 • Weiming Li, Lihui Xue, Xueqian Wang, Gang Li
For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction.
1 code implementation • 1 Mar 2022 • Jinlai Zhang, Weiming Li, Shuang Liang, Hao Wang, Jihong Zhu
We also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose estimation task.
1 code implementation • 25 Apr 2021 • Jinlai Zhang, Lyujie Chen, Binbin Liu, Bo Ouyang, Qizhi Xie, Jihong Zhu, Weiming Li, Yanmei Meng
In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced.
no code implementations • ICCV 2021 • Yamin Mao, Zhihua Liu, Weiming Li, Yuchao Dai, Qiang Wang, Yun-Tae Kim, Hong-Seok Lee
Extensive experiments show that the proposed method achieves the highest ground truth covering ratio compared with other cascade cost volume based stereo matching methods.
no code implementations • CVPR 2013 • Weiming Li, Haitao Wang, Mingcai Zhou, Shandong Wang, Shaohui Jiao, Xing Mei, Tao Hong, Hoyoung Lee, Jiyeun Kim
Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.