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Wanjun Liang - IEEE Xplore Author Profile

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Author details

Wanjun Liang

Publications

1

Citations

0

Publications by Year

20252025

Co-Authors:

Changsheng LiShiye WangGuoren WangZeyu YanYe Yuan

Wanjun Liang

Affiliation

School of Computer Science and Technology

Beijing Institute of Technology

Beijing, China

Publication Topics

CIFAR-100 Dataset,
Context-dependent,
Convolutional Layers,
Fewer Parameters,
Global Context,
Global Information,
Gram Matrix,
Mass Calibration,
Multi-view Data,
Multi-view Learning,
Ordinary Differential Equations,
Parameter Count

Biography

Wanjun Liang received the B.E. degree in computer science and technology from Beijing Institute of Technology (BIT) in 2022, where she is currently pursuing the master’s degree in computer science and technology. Her research interests include multi-modal learning and incremental learning.(Based on document published on 27 November 2024).

Publications

1

Citations

0

Publications by Year

20252025

Co-Authors:

Changsheng Li
Shiye Wang
Guoren Wang
Zeyu Yan
Ye Yuan

Author's Published Works

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Showing 1-5 of 5 results

Conferences (4)

Journals (1)

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Jiaqi Yu(5)
Hong Yang(2)
Peng Zhang(2)
Qiujin Liang(1)
Changsheng Li(1)
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China(2)
Department of Automation, Tsinghua University, Beijing, China(1)
Beijing Institute of Control and Electronics Technology, Beijing, China(1)
Kuaishou Technology, Beijing, China(1)
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China(1)
2024 11th International Conference on Behavioural and Social Computing (BESC)(1)
2024 China Automation Congress (CAC)(1)
2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)(1)
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
IEEE Transactions on Knowledge and Data Engineering(1)
IEEE(5)
Media(1)
Harbin, China(1)
Hyderabad, India(1)
Qingdao, China(1)
Zhuhai, China(1)
Convolutional Layers(2)
Graph Neural Networks(2)
Graph Structure(2)
Node Representations(2)
Synthetic Aperture Radar(2)

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Graph Anomaly Detection via Multi-Scale Reconstruction of Graph Encoder-Decoder Networks

Jiaqi Yu;Hong Yang;Peng Zhang

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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Existing unsupervised graph anomaly detection (GAD) methods can be categorized into reconstruction based methods and contrastive learning based methods. The principle of reconstruction methods is to capture anomalous nodes based on data reconstruction errors. However, existing reconstruction methods are limited to a single scale reconstruction of graph structures, typically predicting edges solely...Show More

Graph Anomaly Detection via Multi-Scale Reconstruction of Graph Encoder-Decoder Networks

Jiaqi Yu;Hong Yang;Peng Zhang

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Open Vocabulary SAR Object Detection with Denoising and Scaled Box Loss

Yuhao Jin;Qiujin Liang;Tao Zhang;Jiaqi Yu

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

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Synthetic aperture radar (SAR) has important applications in military, geology and other fields due to its high resolution and all-day, all-weather operation. However, the inherent speckle noise of SAR images seriously restricts the effectiveness of detection algorithms. To address this challenge, this paper proposes an open vocabulary SAR object detection model that combines a denoising network a...Show More

Open Vocabulary SAR Object Detection with Denoising and Scaled Box Loss

Yuhao Jin;Qiujin Liang;Tao Zhang;Jiaqi Yu

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

Fine-Grained Recognition Framework For Ship Targets In SAR Images Based On Component Architecture

Yongchen Pan;Can Su;Hongcheng Zeng;Yamin Wang;Wei Yang;Chunsheng Li;Jiaqi Yu;Dongliang Li

2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)

Year: 2024 | Conference Paper |

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Applying deep learning network models in SAR image processing has significantly improved speed of target recognition performance. Nevertheless, due to the SAR imaging principle distinct from optical images, these models struggle to distinguish strong scattering characteristics from clutter, resulting in a high false alarm rate in applications of target recognition. To ensure correctness practicall...Show More

Fine-Grained Recognition Framework For Ship Targets In SAR Images Based On Component Architecture

Yongchen Pan;Can Su;Hongcheng Zeng;Yamin Wang;Wei Yang;Chunsheng Li;Jiaqi Yu;Dongliang Li

2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)

Year: 2024 | Conference Paper |

Federated Spectral Graph Neural Networks

Jiaqi Yu;Hong Yang;Zhou Chuan;Peng Zhang;Zhihong Tian

2024 11th International Conference on Behavioural and Social Computing (BESC)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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Federated graph neural networks aim to collabo-ratively train a global graph neural network across multiple clients while preserving privacy constraints. Existing federated graph neural networks fall into the category of federated learning on spatial graph neural networks which rarely consider the challenges of data noise and biased data distribution. To this end, we present a new class of Federat...Show More

Federated Spectral Graph Neural Networks

Jiaqi Yu;Hong Yang;Zhou Chuan;Peng Zhang;Zhihong Tian

2024 11th International Conference on Behavioural and Social Computing (BESC)

Year: 2024 | Conference Paper |

DREAM: Domain-Agnostic Reverse Engineering Attributes of Black-Box Model

Rongqing Li;Jiaqi Yu;Changsheng Li;Wenhan Luo;Ye Yuan;Guoren Wang

IEEE Transactions on Knowledge and Data Engineering

Year: 2024 | Volume: 36, Issue: 12 | Journal Article |

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Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a target black-box model can be exposed through a sequence of queries. There is a crucial limitation: these works assume the training dataset of the target model is known beforehand and leverage this dataset for model att...Show More

DREAM: Domain-Agnostic Reverse Engineering Attributes of Black-Box Model

Rongqing Li;Jiaqi Yu;Changsheng Li;Wenhan Luo;Ye Yuan;Guoren Wang

IEEE Transactions on Knowledge and Data Engineering

Year: 2024 | Volume: 36, Issue: 12 | Journal Article |

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