Antonis A. Armoundas - IEEE Xplore Author Profile

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Cart
- Create Account
- Personal Sign In

Institutional Sign In

Institutional Sign In

ADVANCED SEARCH

Author details

Antonis A. Armoundas

Also published under: A. A. Armoundas

Publications

17

Citations

109

Publications by Year

19972022

Co-Authors:

M. E. BarleyMaya E. BarleyP. A. BelkOlivier BernusBin He

Show All Co-Authors (35)

Antonis A. Armoundas

Also published under: A. A. Armoundas

Affiliation

Cardiovascular Research Center

Massachusetts General Hospital and Institute for Medical Engineering and Science

Massachusetts Institute of Technology, Massachusetts, USA

Publication Topics

Arrhythmia,
Catheter Ablation,
Catheter Tip,
Dipole Locations,
Electrical Activity,
Action Potential,
Action Potential Duration,
Action Potential Prolongation,
Body Surface,
Cardiac Electrical Activity,
Depolarization,
Dipole Orientation

Biography

Antonis A. Armoundas (M'91-SM’06) was born in Mytilini, Greece. He received the B.S. degree in electrical engineering from the National Technical University of Athens, Athens, Greece, in 1991, the M.S. degree in biomedical engineering from Boston University, Boston, MA, USA, in 1994, and the Ph.D. degree in nuclear engineering from the Massachusetts Institute of Technology (MIT), MA, in 1999.,He is currently with the Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA, and an Assistant Professor at Harvard Medical School, while he maintains an appointment at MIT. His research interests include biomedical signal processing, forward and inverse problem solutions, and cellular electrophysiology methods (experimental...

Publications

17

Citations

109

Publications by Year

19972022

Co-Authors:

M. E. Barley
Maya E. Barley
P. A. Belk
Olivier Bernus
Bin He

Show All Co-Authors (35)

Author's Published Works

Search History

Showing 1-25 of 43 results

Journals (22)

Conferences (20)

Early Access Articles (1)

Sort

Filter Results

Show

Open Access Only

Range
Single Year
Hongbing Ma(42)
Liangliang Li(9)
Ding Ni(6)
Zheyuan Wang(6)
Chengzhu Yang(4)
Department of Electronic Engineering, Tsinghua University, Beijing, China(26)
College of Information Science and Engineering, Xinjiang University, Urumqi, China(6)
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China(3)
School of Information Science and Engineering, Xinjiang University, Urumqi, China(2)
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China(2)
IEEE Geoscience and Remote Sensing Letters(8)
2022 7th International Conference on Image, Vision and Computing (ICIVC)(4)
IEEE Transactions on Geoscience and Remote Sensing(4)
IEEE Transactions on Signal Processing(3)
2016 2nd International Conference on Control, Automation and Robotics (ICCAR)(2)
IEEE(41)
TUP(2)
Media(1)
Xi’an, China(4)
Shanghai, China(3)
Athens, Greece(2)
Hong Kong, China(2)
Chengdu, China(1)
Convolutional Neural Network(13)
Remote Sensing(12)
Feature Maps(11)
Attention Mechanism(8)
Convolutional Layers(7)

Select All on Page

Sort By

Results

MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

Wei Huang;Tianren Wu;Xueyu Zhang;Liangliang Li;Ming Lv;Zhenhong Jia;Xiaobin Zhao;Hongbing Ma;Gemine Vivone

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2025 | Early Access Article |

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transform...Show More

MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

Wei Huang;Tianren Wu;Xueyu Zhang;Liangliang Li;Ming Lv;Zhenhong Jia;Xiaobin Zhao;Hongbing Ma;Gemine Vivone

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2025 | Early Access Article |

Fake News Detection: Extendable to Global Heterogeneous Graph Attention Network with External Knowledge

Yihao Guo;Longye Qiao;Zhixiong Yang;Jianping Xiang;Xinlong Feng;Hongbing Ma

Tsinghua Science and Technology

Year: 2025 | Volume: 30, Issue: 3 | Journal Article |

HTML

Distinguishing genuine news from false information is crucial in today's digital era. Most of the existing methods are based on either the traditional neural network sequence model or graph neural network model that has become more popularity in recent years. Among these two types of models, the latter solve the former's problem of neglecting the correlation among news sentences. However, one laye...Show More

Fake News Detection: Extendable to Global Heterogeneous Graph Attention Network with External Knowledge

Yihao Guo;Longye Qiao;Zhixiong Yang;Jianping Xiang;Xinlong Feng;Hongbing Ma

Tsinghua Science and Technology

Year: 2025 | Volume: 30, Issue: 3 | Journal Article |

Cross Teaching-Enhanced Multispectral Remote Sensing Object Detection With Transformer

Jiahe Zhu;Huan Zhang;Simin Li;Shengjin Wang;Hongbing Ma

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2025 | Volume: 18 | Journal Article |

HTML

Remote sensing platforms are often equipped with sensors of multiple spectrums to capture the diverse reflective properties of ground areas, typically including the visible spectrum and the near infrared (NIR) spectrum. Moreover, thermal infrared (TIR) sensors capture the radiated heat of targets and are capable of all-day observation regardless of illumination conditions. By leveraging the comple...Show More

Cross Teaching-Enhanced Multispectral Remote Sensing Object Detection With Transformer

Jiahe Zhu;Huan Zhang;Simin Li;Shengjin Wang;Hongbing Ma

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2025 | Volume: 18 | Journal Article |

Lightweight Remote Sensing Image Super-Resolution via Background-Based Multiscale Feature Enhancement Network

Tianren Wu;Rundong Zhao;Ming Lv;Zhenhong Jia;Liangliang Li;Zheyuan Wang;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2024 | Volume: 21 | Journal Article |

Cited by: Papers (2)

HTML

In the field of remote sensing image super-resolution (RSISR), most methods based on convolutional neural networks (CNNs) tend to focus on high-weight features in the convolutional kernels, thus overlooking low-weight background features. This bias may result in the neglect of some important information in the background. To address this challenge, we propose a background-based multiscale feature ...Show More

Lightweight Remote Sensing Image Super-Resolution via Background-Based Multiscale Feature Enhancement Network

Tianren Wu;Rundong Zhao;Ming Lv;Zhenhong Jia;Liangliang Li;Zheyuan Wang;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2024 | Volume: 21 | Journal Article |

Dual Guidance Enhancing Camouflaged Object Detection via Focusing Boundary and Localization Representation

Songlin Li;Xiuhong Li;Zhe Li;Hongbing Ma;Jiabao Sheng;Boyuan Li

2024 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2024 | Conference Paper |

HTML

Camouflaged object detection (COD) aims to segment objects that blend into their surrounding environment. However, low-level features in the shallow layers of neural networks, although rich in edge information, often contain a significant amount of redundant information, making it difficult to represent boundary details accurately. On the other hand, deep high-level features retain semantic inform...Show More

Dual Guidance Enhancing Camouflaged Object Detection via Focusing Boundary and Localization Representation

Songlin Li;Xiuhong Li;Zhe Li;Hongbing Ma;Jiabao Sheng;Boyuan Li

2024 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2024 | Conference Paper |

Cross Teaching between Single-Spectral and Multi-Spectral Detection Transformers for Remote Sensing Object Detection

Jiahe Zhu;Kaiyue Zhou;Huan Zhang;Shengjin Wang;Hongbing Ma

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Year: 2024 | Conference Paper |

HTML

In recent years, to enhance all-weather observation capabilities, remote sensing platforms have been increasingly equipped with thermal infrared (TIR) sensors in addition to visible spectrum (RGB) sensors. Consequently, remote sensing object detection methods started utilizing images from both modalities to improve detection accuracy. However, the inconsistency of target visibility across the two ...Show More

Cross Teaching between Single-Spectral and Multi-Spectral Detection Transformers for Remote Sensing Object Detection

Jiahe Zhu;Kaiyue Zhou;Huan Zhang;Shengjin Wang;Hongbing Ma

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Year: 2024 | Conference Paper |

Refining 3D Human Mesh via Model-Free Offsets Estimation

Youze Xue;Jiansheng Chen;Hongbing Ma;Huimin Ma

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

HTML

3D human mesh reconstruction from a single RGB image is a challenging task. Existing methods either utilize parametric mesh models to restrain the 3D human structures or directly regress the 3D coordinates of the mesh vertices. The former ones, called model-based methods, usually fail to recover the high variance of human mesh due to limited capacity of parametric models, whereas the latter ones c...Show More

Refining 3D Human Mesh via Model-Free Offsets Estimation

Youze Xue;Jiansheng Chen;Hongbing Ma;Huimin Ma

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

DIAFNet: A Dynamic Interactive Adaptive Fusion Network Based on Enhanced Differential Features

Ru Zhao;Hongbing Ma;Qi Xiao;Gang Shi

IEEE Geoscience and Remote Sensing Letters

Year: 2024 | Volume: 21 | Journal Article |

HTML

Remote sensing change detection (RSCD) seeks to identify areas of interest with changes in photographs from multitemporal remote sensing (RS) that are spatially co-registered, thereby monitoring land surface changes. Identifying imbalanced differences between foreground and background categories is crucial when dealing with limited samples and significant interference. Our letter proposes a dynami...Show More

DIAFNet: A Dynamic Interactive Adaptive Fusion Network Based on Enhanced Differential Features

Ru Zhao;Hongbing Ma;Qi Xiao;Gang Shi

IEEE Geoscience and Remote Sensing Letters

Year: 2024 | Volume: 21 | Journal Article |

Remote Sensing Vehicle Detection Based on SWTPH-YOLOv5

Chenen Xie;HongBing Ma

2023 International Conference on Machine Vision, Image Processing and Imaging Technology (MVIPIT)

Year: 2023 | Conference Paper |

HTML

In transportation complexes, remote sensing technology used to detect vehicles faces challenges such as inaccurate positioning, false alarms, and any detection. To address these issues, this article introduces a remote sensing method based on swtfh yolov5 for detecting vehicles. Firstly, a new feature has been added in the shallow layer to maximize information superiority. Secondly, in order to im...Show More

Remote Sensing Vehicle Detection Based on SWTPH-YOLOv5

Chenen Xie;HongBing Ma

2023 International Conference on Machine Vision, Image Processing and Imaging Technology (MVIPIT)

Year: 2023 | Conference Paper |

A2M: An Amplification-Arbitrary Module for Remote Sensing Image Super-Resolution

Yuan Xue;Zheyuan Wang;Liangliang Li;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

HTML

Remote sensing (RS) image super-resolution (SR) aims to recover high-resolution (HR) images from the corresponding low-resolution (LR) images. In recent years, the SR methods based on convolutional neural networks (CNNs) have achieved incredible performance in case of fixed scale factors (e.g., $\times 2$ , $\times 3$ , and $\times 4$ ). However, these methods need to train a single model for e...Show More

A2M: An Amplification-Arbitrary Module for Remote Sensing Image Super-Resolution

Yuan Xue;Zheyuan Wang;Liangliang Li;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

Infrared Small Target Detection Based on Multidirectional Cumulative Measure

Guofeng Zhang;Askar Hamdulla;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

Cited by: Papers (3)

HTML

Robustness of small target detection is a researchable hotspot in infrared (IR) surveillance system. The residual phenomenon of background clutter is universal in current local comparison methods. The algorithm of sparse low-rank decomposition restoration cannot be applied to the actual situations due to the long time consumption. This letter proposes a multi-directional cumulative measure (MDCM) ...Show More

Infrared Small Target Detection Based on Multidirectional Cumulative Measure

Guofeng Zhang;Askar Hamdulla;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

Transformer Based Remote Sensing Object Detection With Enhanced Multispectral Feature Extraction

Jiahe Zhu;Xu Chen;Huan Zhang;Zelong Tan;Shengjin Wang;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

Cited by: Papers (12)

HTML

As a convention, satellites and drones are equipped with sensors of both the visible light spectrum and the infrared (IR) spectrum. However, existing remote sensing object detection methods mostly use RGB images captured by the visible light camera while ignoring IR images. Even for algorithms that take RGB–IR image pairs as input, they may fail to extract all potential features in both spectrums....Show More

Transformer Based Remote Sensing Object Detection With Enhanced Multispectral Feature Extraction

Jiahe Zhu;Xu Chen;Huan Zhang;Zelong Tan;Shengjin Wang;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

RFCNet: Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network

Yuan Xue;Liangliang Li;Zheyuan Wang;Chenchen Jiang;Minqin Liu;Jiawen Wang;Kaipeng Sun;Hongbing Ma

Tsinghua Science and Technology

Year: 2023 | Volume: 28, Issue: 3 | Journal Article |

Cited by: Papers (3)

HTML

In the field of single remote sensing image Super-Resolution (SR), deep Convolutional Neural Networks (CNNs) have achieved top performance. To further enhance convolutional module performance in processing remote sensing images, we construct an efficient residual feature calibration block to generate expressive features. After harvesting residual features, we first divide them into two parts along...Show More

RFCNet: Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network

Yuan Xue;Liangliang Li;Zheyuan Wang;Chenchen Jiang;Minqin Liu;Jiawen Wang;Kaipeng Sun;Hongbing Ma

Tsinghua Science and Technology

Year: 2023 | Volume: 28, Issue: 3 | Journal Article |

Infrared Small Target Detection With Patch Tensor Collaborative Sparse and Total Variation Constraint

Guofeng Zhang;Askar Hamdulla;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

Cited by: Papers (2)

HTML

Sparse and low-rank modeling has shown the powerful describing abilities to express small targets; however, low-rank model exists the problem of insufficient rank approximation deviation ability and excessive shrinkage, which will lead to inaccurate background estimation. In this letter, a new nonconvex approximation function using the Gaussian model is built toward deeply excavating low-rank info...Show More

Infrared Small Target Detection With Patch Tensor Collaborative Sparse and Total Variation Constraint

Guofeng Zhang;Askar Hamdulla;Hongbing Ma

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

Road Traffic Vehicle Detection Method Using Lightweight YOLOv5 and Attention Mechanism

Yunzhen Wang;Hongbing Ma;Liangliang Li

2022 7th International Conference on Image, Vision and Computing (ICIVC)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

HTML

With the rise in the number of vehicles on the streets, urban road problems are becoming more and more prominent. As the vehicle of the road subject, it is the subject of the problem of intelligent transportation system. In this paper, we discuss the vehicle detection problem in intelligent transportation system, and propose a lightweight YOLOv5 model combining SENet attention mechanism and depthw...Show More

Road Traffic Vehicle Detection Method Using Lightweight YOLOv5 and Attention Mechanism

Yunzhen Wang;Hongbing Ma;Liangliang Li

2022 7th International Conference on Image, Vision and Computing (ICIVC)

Year: 2022 | Conference Paper |

An Improved Cascade R-CNN-Based Target Detection Algorithm for UAV Aerial Images

Hao Huang;Liangliang Li;Hongbing Ma

2022 7th International Conference on Image, Vision and Computing (ICIVC)

Year: 2022 | Conference Paper |

Cited by: Papers (9)

HTML

Target detection in the UAV aerial images is a challenging task in computer vision. Compared with traditional images, UAV images have the characteristics of small and dense targets and complex scenes, which have been a difficult task for target detection. In this paper, an improved algorithm based on cascade R-CNN is proposed to add superclass detection on top of the original one, and then fuse th...Show More

An Improved Cascade R-CNN-Based Target Detection Algorithm for UAV Aerial Images

Hao Huang;Liangliang Li;Hongbing Ma

2022 7th International Conference on Image, Vision and Computing (ICIVC)

Year: 2022 | Conference Paper |

IRNet: An Improved RetinaNet Model for Face Detection

Chenchen Jiang;Hongbing Ma;Liangliang Li

2022 7th International Conference on Image, Vision and Computing (ICIVC)

Year: 2022 | Conference Paper |

Cited by: Papers (5)

HTML

The current face detection methods mainly focus on overlaying network layers to improve the detection accuracy. However, in practical applications, these huge models cannot achieve the real-time detection. In order to solve the aforementioned problems, an improved RetinaNet (IRNet) model for face detection is proposed. In the field of face detection, feature fusion module is a common method to sol...Show More

IRNet: An Improved RetinaNet Model for Face Detection

Chenchen Jiang;Hongbing Ma;Liangliang Li

2022 7th International Conference on Image, Vision and Computing (ICIVC)

Year: 2022 | Conference Paper |

An Improved UNet Model for Foot Ulcer Image Segmentation

Linxin Xing;Liangliang Li;Zheyuan Wang;Hongbing Ma

2022 7th International Conference on Image, Vision and Computing (ICIVC)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

HTML

Extracting accurate morphological features of foot wounds is essential for the correct treatment of foot ulcers (a complication of diabetes). To reduce the workload of healthcare professionals and alleviate the problem that traditional diagnostic methods are heavily influenced by subjective factors, we propose a foot ulcer segmentation method based on an improved UNet model. The proposed method us...Show More

An Improved UNet Model for Foot Ulcer Image Segmentation

Linxin Xing;Liangliang Li;Zheyuan Wang;Hongbing Ma

2022 7th International Conference on Image, Vision and Computing (ICIVC)

Year: 2022 | Conference Paper |

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li;Kai Zhang;Radu Timofte;Luc Van Gool;Fangyuan Kong;Mingxi Li;Songwei Liu;Zongcai Du;Ding Liu;Chenhui Zhou;Jingyi Chen;Qingrui Han;Zheyuan Li;Yingqi Liu;Xiangyu Chen;Haoming Cai;Yu Qiao;Chao Dong;Long Sun;Jinshan Pan;Yi Zhu;Zhikai Zong;Xiaoxiao Liu;Zheng Hui;Tao Yang;Peiran Ren;Xuansong Xie;Xian-Sheng Hua;Yanbo Wang;Xiaozhong Ji;Chuming Lin;Donghao Luo;Ying Tai;Chengjie Wang;Zhizhong Zhang;Yuan Xie;Shen Cheng;Ziwei Luo;Lei Yu;Zhihong Wen;Qi Wul;Youwei Li;Haoqiang Fan;Jian Sun;Shuaicheng Liu;Yuanfei Huang;Meiguang Jin;Hua Huang;Jing Liu;Xinjian Zhang;Yan Wang;Lingshun Long;Gen Li;Yuanfan Zhang;Zuowei Cao;Lei Sun;Panaetov Alexander;Yucong Wang;Minjie Cai;Li Wang;Lu Tian;Zheyuan Wang;Hongbing Ma;Jie Liu;Chao Chen;Yidong Cai;Jie Tang;Gangshan Wu;Weiran Wang;Shirui Huang;Honglei Lu;Huan Liu;Keyan Wang;Jun Chen;Shi Chen;Yuchun Miao;Zimo Huang;Lefei Zhang;Mustafa Ayazoğlu;Wei Xiong;Chengyi Xiong;Fei Wang;Hao Li;Ruimian Wen;Zhijing Yang;Wenbin Zou;Weixin Zheng;Tian Ye;Yuncheng Zhang;Xiangzhen Kong;Aditya Arora;Syed Waqas Zamir;Salman Khan;Munawar Hayat;Fahad Shahbaz Khan;Dandan Gao;Dengwen Zhou;Qian Ning;Jingzhu Tang;Han Huang;Yufei Wang;Zhangheng Peng;Haobo Li;Wenxue Guan;Shenghua Gong;Xin Li;Jun Liu;Wanjun Wang;Dengwen Zhou;Kun Zeng;Hanjiang Lin;Xinyu Chen;Jinsheng Fang

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2022 | Conference Paper |

Cited by: Papers (45)

HTML

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficien...Show More

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li;Kai Zhang;Radu Timofte;Luc Van Gool;Fangyuan Kong;Mingxi Li;Songwei Liu;Zongcai Du;Ding Liu;Chenhui Zhou;Jingyi Chen;Qingrui Han;Zheyuan Li;Yingqi Liu;Xiangyu Chen;Haoming Cai;Yu Qiao;Chao Dong;Long Sun;Jinshan Pan;Yi Zhu;Zhikai Zong;Xiaoxiao Liu;Zheng Hui;Tao Yang;Peiran Ren;Xuansong Xie;Xian-Sheng Hua;Yanbo Wang;Xiaozhong Ji;Chuming Lin;Donghao Luo;Ying Tai;Chengjie Wang;Zhizhong Zhang;Yuan Xie;Shen Cheng;Ziwei Luo;Lei Yu;Zhihong Wen;Qi Wul;Youwei Li;Haoqiang Fan;Jian Sun;Shuaicheng Liu;Yuanfei Huang;Meiguang Jin;Hua Huang;Jing Liu;Xinjian Zhang;Yan Wang;Lingshun Long;Gen Li;Yuanfan Zhang;Zuowei Cao;Lei Sun;Panaetov Alexander;Yucong Wang;Minjie Cai;Li Wang;Lu Tian;Zheyuan Wang;Hongbing Ma;Jie Liu;Chao Chen;Yidong Cai;Jie Tang;Gangshan Wu;Weiran Wang;Shirui Huang;Honglei Lu;Huan Liu;Keyan Wang;Jun Chen;Shi Chen;Yuchun Miao;Zimo Huang;Lefei Zhang;Mustafa Ayazoğlu;Wei Xiong;Chengyi Xiong;Fei Wang;Hao Li;Ruimian Wen;Zhijing Yang;Wenbin Zou;Weixin Zheng;Tian Ye;Yuncheng Zhang;Xiangzhen Kong;Aditya Arora;Syed Waqas Zamir;Salman Khan;Munawar Hayat;Fahad Shahbaz Khan;Dandan Gao;Dengwen Zhou;Qian Ning;Jingzhu Tang;Han Huang;Yufei Wang;Zhangheng Peng;Haobo Li;Wenxue Guan;Shenghua Gong;Xin Li;Jun Liu;Wanjun Wang;Dengwen Zhou;Kun Zeng;Hanjiang Lin;Xinyu Chen;Jinsheng Fang

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2022 | Conference Paper |

Hyperspectral Estimation of Soil Copper Concentration Based on Improved TabNet Model in the Eastern Junggar Coalfield

Yuan Wang;Abdugheni Abliz;Hongbing Ma;Li Liu;Alishir Kurban;Ümüt Halik;Matti Pietikäinen;Wenjuan Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (7)

HTML

China is the largest coal consumer in the world. The massive exploitation and utilization of coal resources have resulted in serious problems of heavy metal pollution and environmental contamination, such as soil degradation, water pollution, crop damage, and even threatening human lives. Therefore, monitoring soil heavy metal pollution quickly and in real time is an urgent task at present. This r...Show More

Hyperspectral Estimation of Soil Copper Concentration Based on Improved TabNet Model in the Eastern Junggar Coalfield

Yuan Wang;Abdugheni Abliz;Hongbing Ma;Li Liu;Alishir Kurban;Ümüt Halik;Matti Pietikäinen;Wenjuan Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Boosting Monocular 3D Human Pose Estimation With Part Aware Attention

Youze Xue;Jiansheng Chen;Xiangming Gu;Huimin Ma;Hongbing Ma

IEEE Transactions on Image Processing

Year: 2022 | Volume: 31 | Journal Article |

Cited by: Papers (20)

HTML

Monocular 3D human pose estimation is challenging due to depth ambiguity. Convolution-based and Graph-Convolution-based methods have been developed to extract 3D information from temporal cues in motion videos. Typically, in the lifting-based methods, most recent works adopt the transformer to model the temporal relationship of 2D keypoint sequences. These previous works usually consider all the j...Show More

Boosting Monocular 3D Human Pose Estimation With Part Aware Attention

Youze Xue;Jiansheng Chen;Xiangming Gu;Huimin Ma;Hongbing Ma

IEEE Transactions on Image Processing

Year: 2022 | Volume: 31 | Journal Article |

FeNet: Feature Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution

Zheyuan Wang;Liangliang Li;Yuan Xue;Chenchen Jiang;Jiawen Wang;Kaipeng Sun;Hongbing Ma

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (43)

HTML

In the field of remote sensing, due to memory consumption and computational burden, the single-image super-resolution (SISR) methods based on deep convolution neural networks (CNNs) are limited in practical application. To address this problem, we propose a lightweight feature enhancement network (FeNet) for accurate remote-sensing image super-resolution (SR). Considering the existence of equipmen...Show More

FeNet: Feature Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution

Zheyuan Wang;Liangliang Li;Yuan Xue;Chenchen Jiang;Jiawen Wang;Kaipeng Sun;Hongbing Ma

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming

Zhiyan Liu;Yuwen Yang;Feifei Gao;Ting Zhou;Hongbing Ma

IEEE Transactions on Communications

Year: 2022 | Volume: 70, Issue: 3 | Journal Article |

Cited by: Papers (34)

HTML

In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral efficiencies of massive multiple-input-multiple-output (MIMO) downlink systems. By employing ResNet to extract features from the channel matrices, two neural networks, i.e., the antenna selection network (ASNet) and the hyb...Show More

Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming

Zhiyan Liu;Yuwen Yang;Feifei Gao;Ting Zhou;Hongbing Ma

IEEE Transactions on Communications

Year: 2022 | Volume: 70, Issue: 3 | Journal Article |

Video Based Shuffling Step Detection for Parkinsonian Patients Using 3D Convolution

Xugang Cao;Youze Xue;Jiansheng Chen;Xiaohe Chen;Yu Ma;Chunhua Hu;Huimin Ma;Hongbing Ma

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Year: 2021 | Volume: 29 | Journal Article |

Cited by: Papers (13)

HTML

Parkinson’s Disease (PD) is a common neurodegenerative disease which impacts millions of people around the world. In clinical treatments, freezing of gait (FoG) is used as the typical symptom to assess PD patients’ condition. Currently, the assessment of FoG is usually performed through live observation or video analysis by doctors. Considering the aging societies, such a manual inspection based a...Show More

Video Based Shuffling Step Detection for Parkinsonian Patients Using 3D Convolution

Xugang Cao;Youze Xue;Jiansheng Chen;Xiaohe Chen;Yu Ma;Chunhua Hu;Huimin Ma;Hongbing Ma

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Year: 2021 | Volume: 29 | Journal Article |

2-D Learned Proximal Gradient Algorithm for Fast Sparse Matrix Recovery

Chengzhu Yang;Yuantao Gu;Badong Chen;Hongbing Ma;Hing Cheung So

IEEE Transactions on Circuits and Systems II: Express Briefs

Year: 2021 | Volume: 68, Issue: 4 | Journal Article |

Cited by: Papers (4)

HTML

Many real-world problems can be modeled as sparse matrix recovery from two-dimensional (2D) measurements, which is recognized as one of the most important topics in signal processing community. Benefited from the roaring success of compressed sensing, many classical iterative algorithms can be directly applied or reinvented for matrix recovery, though they are computationally expensive. To allevia...Show More

2-D Learned Proximal Gradient Algorithm for Fast Sparse Matrix Recovery

Chengzhu Yang;Yuantao Gu;Badong Chen;Hongbing Ma;Hing Cheung So

IEEE Transactions on Circuits and Systems II: Express Briefs

Year: 2021 | Volume: 68, Issue: 4 | Journal Article |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.