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Unsupervised social network embedding via adaptive specific mappings

Frontiers of Computer Science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

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Underwater target recognition based on adaptive multi-feature fusion network

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Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A data representation method using distance correlation

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Frontiers of Computer Science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Spatial-Temporal Graph Deviation Neural Networks for Anomaly Detection in Time Series

Spatial-Temporal Graph Deviation Neural Networks for Anomaly...

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2023 International Conference on Electronic, Electrical and Information Technology, EEIT 2023

作者： Guo, Ziyu Kong, Weiyang Liu, Yubao Sun Yat-Sen University Guang Zhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Anomaly detection in time series data (e.g., sensor data) is becoming a fundamental research problem that has various applications. Due to the complex inter-sensor relationships, it is challenging to detect anomalous events such as system faults and attacks hidden the high-dimensional time series. Recent advancements in deep learning approaches such as Graph Neural Networks (GNN) have greatly improved anomaly detection performance in time series data. However, existing methods usually use separate components to capture spatial and temporal dependence relationship and ignore the heterogeneities in spatial-temporal data which motivates us to capture them together. In this paper, we propose a novel approach STGDNN (short for Spatial-Temporal Graph Deviation Neural Networks) that learns the spatial-temporal dependence relationship together in structure learning of graph neural networks. In addition, we use the learned spatial-temporal graph structure and attention weights to explain the detected anomalies. The experiments on three real world datasets show our superiority in detection accuracy, anomaly diagnosis, and model interpretation compared with state-of-the-art methods. © Published under licence by IOP Publishing Ltd.

关键词： Anomaly detection

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Anima2: Cross-Species Animal Animation through Image-to-Video Synthesis with Subject Alignment

Anima2: Cross-Species Animal Animation through Image-to-Vide...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Xu, Yuanfeng Chen, Yuhao Huang, Zhongzhan He, Zijian Wang, Guangrun Lin, Liang Sun Yat-sen University China Peng Cheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350368741

Recent video editing advancements rely on accurate pose sequences to animate human actors. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differ greatly from that of a pig due to their distinct body structures). In this paper, we present Anima2, a zero-shot diffusion-based video generator to address this issue, aiming to accurately ANIMAte ANIMAls while preserving the background. The key technique involves two-fold subject alignment. First, we improve appearance feature extraction by integrating a Laplacian detail booster and a prompt-tuning identity extractor. They capture essential appearance information, including identity and fine details. Second, we align shape features and address conflicts from differing animals by introducing a scale-information remover and an adaptive rescaling module. They both enhance subject alignment for accurate cross-species animation. Additionally, we introduce two high-quality animal video datasets with diverse species to benchmark cross-species animation. Trained on these extensive datasets, our model directly generates videos with accurate movements, consistent appearances, and high-fidelity frames, eliminating the need for test-time training. Extensive experiments demonstrate our method's superiority in cross-species animation, showcasing robust adaptability and generality. © 2025 IEEE.

关键词： cross-species subject alignment video editing

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Cross-Modal Hash Retrieval with Category Semantics 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Xu, Mengying Lai, Hanjiang Yin, Jian Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (数字)9783031533051

ISBN: (纸本)9783031533044

With the multi-medal resource springing up, the field of cross-modal research has witnessed rapid advancement. Among these, Cross-Modal Hash retrieval has attracted attention due to its time-saving capabilities during the retrieval process. However, most existing cross-modal hash retrieval methods focus on capturing the relationship between modalities and often train in a one-to-one correspondence manner. Consequently, they often neglect the comprehensive distribution of the entire dataset. In response, we propose a novel approach that introduces semantic relationships between categories to capture class relationships and enable the measurement of the full distribution. First, the original image and text are encoded to obtain respective representations. Then, these are fused to generate a joint representation, which is passed through a classification head to obtain category semantics. The semantic information is used to allocate hash centers from a Hadamard matrix. Meanwhile, the image and text representations are fed into a hash layer to produce hash codes. We train the network with a modal matching loss for alignment, as well as center and class loss to capture the full data distribution. Extensive evaluations on two benchmark databases demonstrate that our approach achieves competitive performance compared to state-of-the-art methods. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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LLDB: Efficient Low-Light Image Enhancement with Difffusion Bridge

LLDB: Efficient Low-Light Image Enhancement with Difffusion ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Ma, Junlong Yue, Conghan Peng, Zhengwei Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

This paper investigates a low-light image enhancement method based on the diffusion bridge framework. Currently, low-light image enhancement tasks still face challenges in noise reduction and detail restoration, and existing diffusion model methods are time-consuming and have unstable diffusion processes. We conducted an in-depth study of diffusion bridge theory, integrating the advantages of the end-to-end paradigm of diffusion bridge theory and employing a nonlinear activation network to further enhance the performance of low-light image enhancement tasks. Additionally, we used a Gamma correction module for fine-tuning low-light images, significantly improving performance with almost no extra computational cost. Experiments show that Low-Light image enhancement with Difffusion Bridge (LLDB) far surpasses other methods on LOLv1 and LOLv2 datasets. Code is available at https://***/M-Chase/LLDB. © 2025 IEEE.

关键词： Diffusion Bridge Diffusion Model Low-Light image Enhancement

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Robust primary quantization step estimation on resized and double JPEG compressed images

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Multimedia Tools and Applications 2025年第12期84卷 11097-11118页

作者： Zhang, Lei Chen, XuGuang Niu, YaKun Zuo, XianYu Wang, Huaqing School of Computer and Information Engineering Henan Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng Henan Henan Kaifeng475004 China National Internet Emergency Center Henan Zhengzhou450000 China

As one of the most important forensic tasks, reconstruction of the original information in tampered images is a key step for tampering detection and localization. Currently, a number of methods have been designed to estimate the primary quantization steps of double compressed JPEG images. However, the estimation in the presence of resizing operation remains a challenge. In this paper, we propose a robust primary quantization steps estimation method on resized and double JPEG compressed images. Specifically, the distribution of Discrete Cosine Transform (DCT) coefficients is firstly analyzed on the inverse resized image. Then, a maximum likelihood function together with a filtering strategy is designed to obtain the primary quantization step on Alternating Current (AC) bands. In addition, we find the prominent peak in the Discrete Fourier Transform (DFT) spectrum of the distribution of Direct Current (DC) coefficients is nonlinearly related to the step. Based on this observation, a mapping function derived from the geometric fitting is proposed to estimate the step on DC band. Experimental results demonstrate the proposed method provides superior estimation performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image compression

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SymGaussian: Occluded Human Rendering with Multi-scale Symmetry Feature from Monocular Video

SymGaussian: Occluded Human Rendering with Multi-scale Symme...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Jiang, Zekai Duan, Tong Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

The growing demand for high-quality 3D human rendering in real-world applications highlights significant challenges. These challenges are particularly evident in dealing with occlusion in monocular video. Previous methods often rely on controlled datasets and overlook the inherent symmetry of the human body, leading to incomplete rendering in occluded areas. To address these limitations, we propose SymGaussian, a novel Gaussian Splatting-based approach for rendering occluded human from monocular video. We introduce Multi-scale Symmetry Feature to compensate for lost information in occluded areas, along with a Projective Texture Mapping method that efficiently encodes 2D appearance while preserving 3D perception. Experiments show that SymGaussian outperforms state-of-the-art methods in rendering quality, while achieving rapid training speed and real-time rendering capability exceeding 200 FPS. © 2025 IEEE.

关键词： 3D Gaussian Splatting Multi-scale Symmetry Feature Occluded Human Rendering Texture Mapping

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Infusing knowledge into CLIP via low-rank matrix for video-text retrieval

Infusing knowledge into CLIP via low-rank matrix for video-t...

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2024 International Conference on Image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Zhang, Daqing Yin, Jian School of Artificial Intelligence Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9781510680425

Video-text retrieval has been a crucial task with the exponential growth of video. Recent methods that leverage the pretrained CLIP model in the video-text retrieval task have demonstrated remarkable performance, surpassing many approaches trained on large-scale video-text datasets. However, these works ignore the performance-efficiency trade-off in the pursuit of better performance. Additionally, a completely new model is typically necessary for each task because many existing works fully fine-tune the pre-trained backbone. Therefore, to yield a compact and transferable model, we propose LoCLIP, a framework that transfers knowledge from CLIP in a parameter-efficient manner. Inspired by LoRA, we incorporate only a small set of trainable low-rank matrices per task, allowing adaptation to new tasks by simply replacing these matrices. In this way, we can acquire task-specific knowledge without compromising the prior knowledge stored in the pre-trained backbone. To demonstrate the effectiveness of our LoCLIP, we conduct extensive experiments and achieve comparable performance with state-of-the-art CLIP-based video-text retrieval methods while updating only a few parameters. © 2024 SPIE.

关键词： Video recording

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