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Beichen Zhou - IEEE Xplore Author Profile

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Author details

Beichen Zhou

Publications

4

Citations

24

Publications by Year

20212025

Co-Authors:

Qi BiWei JiKun QinGui-Song XiaQinghao Ye

Show All Co-Authors (6)

Beichen Zhou

Affiliation

State Key Laboratory LIESMARS

Wuhan University

Wuhan, China

Publication Topics

Aerial Images,
Aerial Scene,
Aerial Scene Classification,
Benchmark,
Convolutional Layers,
Convolutional Neural Network,
Scene Classification,
Aerial Image Dataset,
Convolutional Features,
Deep Learning,
Dilated Convolution,
Dilation Rate

Biography

Beichen Zhou received the B.S. degree from Wuhan University, Wuhan, China, in 2022, where he is currently pursuing the M.S. degree.,His research interests include aerial image object detection and deep learning.(Based on document published on 25 August 2022).

Publications

4

Citations

24

Publications by Year

20212025

Co-Authors:

Qi Bi
Wei Ji
Kun Qin
Gui-Song Xia
Qinghao Ye

Show All Co-Authors (6)

Author's Published Works

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Showing 1-4 of 4 results

Conferences (2)

Journals (2)

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Beichen Zhou(4)
Qi Bi(3)
Jingjun Yi(2)
Gui-Song Xia(2)
Qinghao Ye(1)
School of Remote Sensing and Information Engineering, Wuhan University, China(2)
Department of Computer Science and Engineering, University of California, San Diego, CA, USA(1)
Wuhan University, Wuhan, China(1)
School of Medicine, Yale University, New Haven, CT, USA(1)
State Key Laboratory of LIESMARS, National Engineering Research Center for Multimedia Software, School of Computer Science, Institute of Artificial Intelligence, Wuhan University, Wuhan, China(1)
2022 IEEE International Conference on Image Processing (ICIP)(1)
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
IEEE Transactions on Geoscience and Remote Sensing(1)
IEEE Transactions on Image Processing(1)
IEEE(4)
Bordeaux, France(1)
Toronto, ON, Canada(1)
Aerial Images(4)
Aerial Scene(3)
Aerial Scene Classification(3)
Benchmark(3)
Convolutional Layers(3)

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Universal Fine-Grained Visual Categorization by Concept Guided Learning

Qi Bi;Beichen Zhou;Wei Ji;Gui-Song Xia

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

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Existing fine-grained visual categorization (FGVC) methods assume that the fine-grained semantics rest in the informative parts of an image. This assumption works well on favorable front-view object-centric images, but can face great challenges in many real-world scenarios, such as scene-centric images (e.g., street view) and adverse viewpoint (e.g., object re-identification, remote sensing). In s...Show More

Universal Fine-Grained Visual Categorization by Concept Guided Learning

Qi Bi;Beichen Zhou;Wei Ji;Gui-Song Xia

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

A Multi-Stage Duplex Fusion Convnet for Aerial Scene Classification

Jingjun Yi;Beichen Zhou

2022 IEEE International Conference on Image Processing (ICIP)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Existing deep learning based methods effectively prompt the performance of aerial scene classification. However, due to the large amount of parameters and computational cost, it is rather difficult to apply these methods to multiple real-time remote sensing applications such as on-board data preception on drones and satellites. In this paper, we address this task by developing a light-weight ConvN...Show More

A Multi-Stage Duplex Fusion Convnet for Aerial Scene Classification

Jingjun Yi;Beichen Zhou

2022 IEEE International Conference on Image Processing (ICIP)

Year: 2022 | Conference Paper |

All Grains, One Scheme (AGOS): Learning Multigrain Instance Representation for Aerial Scene Classification

Qi Bi;Beichen Zhou;Kun Qin;Qinghao Ye;Gui-Song Xia

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (19)

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Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly and 2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how to effectively perceive the region of interests (RoIs) from a variety of sizes and build more discriminative representation from such complicated object distribution is vital to un...Show More

All Grains, One Scheme (AGOS): Learning Multigrain Instance Representation for Aerial Scene Classification

Qi Bi;Beichen Zhou;Kun Qin;Qinghao Ye;Gui-Song Xia

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Differential Convolution Feature Guided Deep Multi-Scale Multiple Instance Learning for Aerial Scene Classification

Beichen Zhou;Jingjun Yi;Qi Bi

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2021 | Conference Paper |

Cited by: Papers (4)

HTML

Aerial image classification is challenging for current deep learning models due to the varied geo-spatial object scales and the complicated scene spatial arrangement. Thus, it is necessary to stress the key local feature response from a variety of scales so as to represent discriminative convolutional features. In this paper, we propose a deep multi-scale multiple instance learning (DMSMIL) framew...Show More

Differential Convolution Feature Guided Deep Multi-Scale Multiple Instance Learning for Aerial Scene Classification

Beichen Zhou;Jingjun Yi;Qi Bi

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2021 | Conference Paper |

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