Hui Zhou - IEEE Xplore Author Profile

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Author details

Hui Zhou

Publications

11

Citations

1,018

Publications by Year

20182024

Co-Authors:

Jian ChengFangzhou HongLingdong KongHongsheng LiQuanquan Li

Show All Co-Authors (33)

Hui Zhou

Affiliation

SenseTime Research, China

Publication Topics

LiDAR Point Clouds,
Point Cloud,
Semantic Segmentation,
Large-scale Datasets,
Distribution Of Points,
LiDAR Point,
LiDAR Sensor,
Point Cloud Segmentation,
Segmentation Task,
Validation Set,
2D Convolutional Network,
2D Space

Biography

Hui Zhou received the bachelor's degree with the University of Science and Electronic Technology of China, in 2015. He is currently a research assistant in the Department of Electronic Engineering, Chinese University of Hong Kong. His research interests include computer vision and machine learning.(Based on document published on 29 August 2017).

Publications

11

Citations

1,018

Publications by Year

20182024

Co-Authors:

Jian Cheng
Fangzhou Hong
Lingdong Kong
Hongsheng Li
Quanquan Li

Show All Co-Authors (33)

Author's Published Works

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Showing 1-4 of 4 results

Journals (3)

Conferences (1)

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Junjie Ren(4)
Jing Bai(4)
Zhu Xiao(3)
Licheng Jiao(3)
Zheng Chen(2)
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China(2)
Shenzhen Research Institute, Hunan University, Shenzhen, China(2)
College of Computer Science and Electronics Engineering, Hunan University, Changsha, China(1)
China mobile tietong corporation shanxi branch, Taiyuan, China(1)
School of Artificial Intelligence and the Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, Xidian University, Xi’an, China(1)
IEEE Transactions on Geoscience and Remote Sensing(1)
IEEE Transactions on Instrumentation and Measurement(1)
IEEE Transactions on Neural Networks and Learning Systems(1)
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium(1)
IEEE(4)
Kuala Lumpur, Malaysia(1)
Object Detection(3)
Bounding Box(2)
Complex Background(2)
Feature Maps(2)
Remote Sensing Images(2)

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An Interpretable Explanation Approach for Signal Modulation Classification

Jing Bai;Yuheng Lian;Yiran Wang;Junjie Ren;Zhu Xiao;Huaji Zhou;Licheng Jiao

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Cited by: Papers (7)

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Signal modulation classification (SMC) has attracted extensive attention for its wide application in the military and civil fields. The current direction of combining deep-learning (DL) technology with wireless communication technology is developing hotly. DL models are riding high in the field of SMC with their highly abstract feature extraction capability. However, most DL models are decision-ag...Show More

An Interpretable Explanation Approach for Signal Modulation Classification

Jing Bai;Yuheng Lian;Yiran Wang;Junjie Ren;Zhu Xiao;Huaji Zhou;Licheng Jiao

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Localizing From Classification: Self-Directed Weakly Supervised Object Localization for Remote Sensing Images

Jing Bai;Junjie Ren;Zhu Xiao;Zheng Chen;Chengxi Gao;Talal Ahmed Ali Ali;Licheng Jiao

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 12 | Journal Article |

Cited by: Papers (7)

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In recent years, object localization and detection methods in remote sensing images (RSIs) have received increasing attention due to their broad applications. However, most previous fully supervised methods require a large number of time-consuming and labor-intensive instance-level annotations. Compared with those fully supervised methods, weakly supervised object localization (WSOL) aims to recog...Show More

Localizing From Classification: Self-Directed Weakly Supervised Object Localization for Remote Sensing Images

Jing Bai;Junjie Ren;Zhu Xiao;Zheng Chen;Chengxi Gao;Talal Ahmed Ali Ali;Licheng Jiao

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 12 | Journal Article |

Few-Shot SAR Ship Image Detection Using Two-Stage Cross-Domain Transfer Learning

Xu Wang;Huaji Zhou;Zheng Chen;Jing Bai;Junjie Ren;Jiao Shi

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

Cited by: Papers (4)

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Synthetic Aperture Radar is superior to optical sensors in that it can identify ships at all hours and on all days. Deep learning-based object detection relies on huge amounts of data, yet SAR ship images are challenging to obtain and label. A few-shot cross-domain transfer learning approach for SAR image ship detection is used in this paper. It is divided into two stages: the first uses a large v...Show More

Few-Shot SAR Ship Image Detection Using Two-Stage Cross-Domain Transfer Learning

Xu Wang;Huaji Zhou;Zheng Chen;Jing Bai;Junjie Ren;Jiao Shi

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Year: 2022 | Conference Paper |

Object Detection in Large-Scale Remote-Sensing Images Based on Time-Frequency Analysis and Feature Optimization

Jing Bai;Junjie Ren;Yujia Yang;Zhu Xiao;Wentao Yu;Vincent Havyarimana;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (17)

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Recently, optical remote-sensing images have been steadily growing in size, as they contain massive data and complex backgrounds. This trend presents several problems for object detection, for example, increased computation time and memory consumption and more false positives due to the complex backgrounds of large-scale images. Inspired by deep neural networks combined with time-frequency analysi...Show More

Object Detection in Large-Scale Remote-Sensing Images Based on Time-Frequency Analysis and Feature Optimization

Jing Bai;Junjie Ren;Yujia Yang;Zhu Xiao;Wentao Yu;Vincent Havyarimana;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

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