no code implementations • 5 Feb 2025 • Xiaoshuai Hao, Yunfeng Diao, Mengchuan Wei, Yifan Yang, Peng Hao, Rong Yin, HUI ZHANG, Weiming Li, Shu Zhao, Yu Liu
To address these issues, we propose MapFusion, a novel multi-modal Bird's-Eye View (BEV) feature fusion method for map construction.
no code implementations • 28 Aug 2024 • Jianjun Ma, Yuheng Song, Mingxia Zhang, Guohao Liu, Weiming Li, John F. Federici, Daniel M. Mittleman
With the growing demand for higher wireless data rates, the interest in extending the carrier frequency of wireless links to the terahertz (THz) range has significantly increased.
1 code implementation • 8 Jul 2024 • Weiming Li, Manni Duan, Dong An, Yan Shao
The experimental results reveal that the layout understanding ability of LLMs is mainly introduced by the coding data for pretraining, which is further enhanced at the instruction-tuning stage.
no code implementations • 18 Jun 2024 • Xiaoshuai Hao, Mengchuan Wei, Yifan Yang, Haimei Zhao, HUI ZHANG, Yi Zhou, Qiang Wang, Weiming Li, Lingdong Kong, Jing Zhang
These insights provide a pathway for developing more reliable HD map construction methods, which are essential for the advancement of autonomous driving technology.
no code implementations • 3 May 2024 • Chengyang Zhang, Weiming Li, Gang Li, Huina Song, Zhaohui Song, Xueqian Wang, Antonio Plaza
Detection of changes in heterogeneous remote sensing images is vital, especially in response to emergencies like earthquakes and floods.
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.
1 code implementation • 21 Mar 2024 • Yangchun Zhang, Qiang Liu, Weiming Li, Yirui Zhou
Criticism 3 lies in Unsatisfactory Proof from the Perspective of Potential Equilibrium.
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, Baocheng Geng, Pramod K. Varshney
To enhance the interpretability of existing neural networks for CD, we propose a knowledge-data-driven heterogeneous CD method based on a copula-guided neural network, named NN-Copula-CD.
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.