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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

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Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization

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Computers, Materials & Continua 2025年第3期82卷 5035-5055页

作者： Yonghang Yan Xin Xie Hengyi Ren Ying Cao Hongwei Chang Henan Key Laboratory of Big Data Analysis and Processing Computer and Information EngineeringHenan UniversityKaifeng475004China College of Information Science and Technology Nanjing Forestry UniversityNanjing210037China Henan Branch China Life Insurance Co.Ltd.Zhengzhou450000China

Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data *** Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security ***,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across *** heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse *** address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated *** algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature ***,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model *** conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.

关键词： Fingerprint recognition privacy protection federated learning adaptive weight adjustment

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Causal-TSF: A Causal Intervention Approach to Mitigate Confounding Bias in Time Series Forecasting

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IEEE Transactions on Knowledge and data Engineering 2025年第6期37卷 3205-3219页

作者： Gong, Qinkang Pan, Yan Lai, Hanjiang Qiu, Rongbang Yin, Jian Sun Yat-sen University School of Artificial Intelligence Zhuhai519082 China Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China Sun Yat-sen University School of Computer Science Guangdong510006 China

Time series forecasting, aiming to learn models from historical data and predict future values in time series, is a fundamental research topic in machine learning. However, few efforts have been devoted to addressing the confounding effects in time series data, e.g., the historical data are affected by some hidden surrounding factors (i.e., confounders), leading to biased forecasting models for future data. This paper presents a causal intervention approach to eliminate the bias that is raised by some hidden confounders. By using a causal graph, we illustrate why hidden confounders can bring bias in time series forecasting and how to tackle it. We implement causal intervention by a deep architecture that consists of two modules, a Confounders Estimation module to estimate the hidden confounders and a Debiasing module to eliminate the confounding bias in the forecasting model via sampling on confounders. We conduct comprehensive evaluations on various time series datasets. The experiment results indicate that the proposed method can reduce the negative confounding effects in time series data, and it achieves superior gains over state-of-the-art baselines for time series forecasting. © 1989-2012 IEEE.

关键词： Prediction models

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Edge-Cloud Cooperation-Driven Intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

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IEEE Internet of Things Journal 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi’an710121 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xidian University Advanced Manufacturing Technology Innovation Center Guangzhou Institute of Technology Guangzhou510555 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou510006 China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy. © 2014 IEEE.

关键词： Circular economy

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Wind turbine detection based on high spatial resolution four-band reflectance images 6

Wind turbine detection based on high spatial resolution four...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Deng, Fengying Qiao, Baojun Li, Kaitao Zhao, Limin Chen, Xingfeng Li, Jiaguo Liu, Jun Sun, Yuan Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Aerospace Information Research Institute Chinese Academy of Science Beijing China School of Space Information Space Engineering University Beijing China

ISBN: (纸本)9781510688018

Wind turbine detection is essential for the power industry and regulatory agencies to efficiently and accurately determine their number and spatial distribution. Under complex underlying surface conditions, the detection accuracy of conventional methods is low, especially when dealing with multi-scale wind turbine targets. In addition, the underutilization of the near-infrared (NIR) band limits the capability of optical remote sensing sensors in wind turbine detection. To address the above problems, this paper proposes a wind turbine detection algorithm YOLOv8m-BD based on high spatial resolution four-band reflectance images using YOLOv8m as the baseline model. Firstly, the four-band image is input through channel adaptation modification. Next, the CSPDarknet53 to 2-stage FPN (C2f) module is enhanced by introducing deformable convolution (DCN) to expand the receptive field. Finally, the effectiveness of our design was validated through extensive experiments on a self-built wind turbine dataset. Compared to the YOLOv8m baseline model that only inputs RGB three bands, the improved YOLOv8m-BD achieves an accuracy of 96.4% and a false alarm rate of 2.0%, an increase of 3.4% in accuracy and a decrease of 2.5% in false alarm rate. © 2025 SPIE.

关键词： Proximity sensors

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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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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Training Solid modeling Three-dimensional displays Streaming media Signal processing Rendering (computer graphics) Real-time systems Acoustics Speech processing

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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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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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

关键词： Bridges Computational modeling Noise reduction Diffusion processes Speech enhancement Signal processing Diffusion models image restoration image enhancement Faces

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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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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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 Anima 2 , 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.

关键词： Training Accuracy Laplace equations Animals Shape Signal processing Animation Feature extraction Information leakage Speech processing

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3DAffordSplat: Efficient Affordance Reasoning with 3D Gaussians

arXiv

引用

arXiv 2025年

作者： Wei, Zeming Lin, Junyi Liu, Yang Chen, Weixing Luo, Jingzhou Li, Guanbin Lin, Liang Sun Yat-sen University China Peng Cheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China

3D affordance reasoning is essential in associating human instructions with the functional regions of 3D objects, facilitating precise, task-oriented manipulations in embodied AI. However, current methods, which predominantly depend on sparse 3D point clouds, exhibit limited generalizability and robustness due to their sensitivity to coordinate variations and the inherent sparsity of the data. By contrast, 3D Gaussian Splatting (3DGS) delivers high-fidelity, real-time rendering with minimal computational overhead by representing scenes as dense, continuous distributions. This positions 3DGS as a highly effective approach for capturing fine-grained affordance details and improving recognition accuracy. Nevertheless, its full potential remains largely untapped due to the absence of large-scale, 3DGS-specific affordance datasets. To overcome these limitations, we present 3DAffordSplat, the first large-scale, multimodal dataset tailored for 3DGS-based affordance reasoning. This dataset includes 23,677 Gaussian instances, 8,354 point cloud instances, and 6,631 manually annotated affordance labels, encompassing 21 object categories and 18 affordance types. Building upon this dataset, we introduce AffordSplatNet, a novel model specifically designed for affordance reasoning using 3DGS representations. AffordSplatNet features an innovative cross-modal structure alignment module that exploits structural consistency priors to align 3D point cloud and 3DGS representations, resulting in enhanced affordance recognition accuracy. Extensive experiments demonstrate that the 3DAffordSplat dataset significantly advances affordance learning within the 3DGS domain, while AffordSplatNet consistently outperforms existing methods across both seen and unseen settings, highlighting its robust generalization capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

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Enhanced Control for Diffusion Bridge in image Restoration

Enhanced Control for Diffusion Bridge in Image Restoration

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image restoration refers to the process of restoring a damaged low-quality image back to its corresponding high-quality image. Recently, a special type of diffusion bridge model has achieved more advanced results in image restoration. It can transform the direct mapping from low-quality to high-quality images into a diffusion process, restoring low-quality images through a reverse process. However, the current diffusion bridge restoration models do not emphasize the idea of conditional control, which may affect performance. This paper introduces the ECDB model enhancing the control of the diffusion bridge with low-quality images as conditions. Moreover, in response to the characteristic of diffusion models having low denoising level at larger values of t, we also propose a Conditional Fusion Schedule, which more effectively handles the conditional feature information of various modules. Experimental results prove that the ECDB model has achieved state-of-the-art results in many image restoration tasks, including deraining, inpainting and super-resolution. Code is avaliable at https://***/Hammour-steak/ECDB.

关键词： Bridges Schedules Noise reduction Superresolution Diffusion processes Transforms Predictive models Feature extraction image restoration Speech processing

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