Search Results for author: Weiming Li

Found 15 papers, 5 papers with code

MapFusion: A Novel BEV Feature Fusion Network for Multi-modal Map Construction

no code implementations5 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.

Autonomous Driving

Terahertz Channels in Atmospheric Conditions: Propagation Characteristics and Security Performance

no code implementations28 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.

Large Language Models Understand Layout

1 code implementation8 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.

Question Answering Visual Question Answering

Is Your HD Map Constructor Reliable under Sensor Corruptions?

no code implementations18 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.

Autonomous Driving Data Augmentation

DOCTR: Disentangled Object-Centric Transformer for Point Scene Understanding

1 code implementation25 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.

Decoder Object +1

DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation

no code implementations20 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.

3D Hand Pose Estimation regression

DVI-SLAM: A Dual Visual Inertial SLAM Network

no code implementations25 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.

Simultaneous Localization and Mapping

NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images

no code implementations30 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.

Binary Classification Change Detection

MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images

no code implementations14 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.

Change Detection

3D Adversarial Attacks Beyond Point Cloud

1 code implementation25 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.

Adversarial Attack

UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching

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.

Stereo Matching

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