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arXiv 2025年

作者： Chang, Laibin Wang, Yunke Du, Bo Xu, Chang The School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The School of Computer Science The University of Sydney Australia

Underwater imaging often suffers from significant visual degradation, which limits its suitability for subsequent applications. While recent underwater image enhancement (UIE) methods rely on the current advances in deep neural network architecture designs, there is still considerable room for improvement in terms of cross-scene robustness and computational efficiency. Diffusion models have shown great success in image generation, prompting us to consider their application to UIE tasks. However, directly applying them to UIE tasks will pose two challenges, i.e., high computational budget and color unbalanced perturbations. To tackle these issues, we propose DiffColor, a distribution-aware diffusion and cross-spectral refinement model for efficient UIE. Instead of diffusing in the raw pixel space, we transfer the image into the wavelet domain to obtain such low-frequency and high-frequency spectra, it inherently reduces the image spatial dimensions by half after each transformation. Unlike single-noise image restoration tasks, underwater imaging exhibits unbalanced channel distributions due to the selective absorption of light by water. To address this, we design the Global Color Correction (GCC) module to handle the diverse color shifts, thereby avoiding potential global degradation disturbances during the denoising process. For the sacrificed image details caused by underwater scattering, we further present the Cross-Spectral Detail Refinement (CSDR) to enhance the high-frequency details, which are integrated with the low-frequency signal as input conditions for guiding the diffusion. This way not only ensures the high-fidelity of sampled content but also compensates for the sacrificed details. Comprehensive experiments demonstrate the superior performance of DiffColor over state-of-the-art methods in both quantitative and qualitative evaluations. © 2025, CC BY.

关键词： Budget control

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HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Quality Assessment Metric

HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Q...

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

作者： Yuanming Zheng Jiaxuan Yao Xiangyu Deng Yuhong Yang Ruiqi Liao Weiping Tu Cedar Lin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spatial audio quality evaluation is essential for applications like virtual and augmented reality, where accurate sound reproduction enhances user immersion. While subjective listening tests are the gold standard, they are costly and time-consuming. To address this, we propose HAPG-SAQAM, an objective metric for assessing timbre quality, spatial quality, and overall quality of binaural audio, guided by human auditory perception. Our contributions include: (1) the Multi-scale Auditory Guided Feature Extraction (MAGFE) module, incorporating gammatone frequency cepstral coefficients for better alignment with human perception; (2) Perceptual Weighted Loss (PWL), optimizing the weighting of timbre quality (TQ) and spatial quality (SQ) loss based on subjective test data; and (3) data augmentation techniques to enhance robustness by amplifying perceptual distortions. Experimental results show HAPG-SAQAM improves correlation with subjective scores by 10%, with ablation studies confirming the contributions of its components to enhanced spatial and overall audio quality.

关键词： Measurement Accuracy Spatial audio Performance gain Feature extraction Robustness Quality assessment Timbre Speech processing Optimization

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A Survey of Adversarial Examples in Computer Vision:Attack,Defense,and Beyond

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Wuhan University Journal of Natural Sciences 2025年第1期30卷 1-20页

作者： XU keyizhi LU Yajuan WANG Zhongyuan LIANG Chao School of Computer Science Wuhan UniversityWuhan 430072HubeiChina National Engineering Research Center for Multimedia Software(NERCMS) Wuhan UniversityWuhan 430072HubeiChina Key Laboratory of Multimedia and Network Communication Engineering Hubei ProvinceWuhan UniversityWuhan 430072HubeiChina School of Cyber Science and Engineering Wuhan UniversityWuhan 430072HubeiChina

Recent years have witnessed the ever-increasing performance of Deep Neural networks(DNNs)in computer vision ***,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input *** this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial ***,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.

关键词： computer vision adversarial examples adversarial attack adversarial defense

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic- like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. Firstly, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic- like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. The source code will be made publicly available. ©1979-2012 IEEE.

关键词： Latent semantic analysis

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A two Stage Mixed Integer Programming Model for Distributionally Robust State Based Non-Intrusive Load Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Chen Chai, Zimo Yang, Linfeng University of Shanghai for Science and Technology School of Mechanical Engineering Shanghai China Guangxi University School of Computer Electronics and Information the Guangxi Key Laboratory of Multimedia Communication and Network Technology Nanning China

This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this paper model introduces fewer integer variables and richer absolute error function of load decomposition, which makes the power state selection of each device more accurate and the power consumption more accurate fitting. First, to tackle the issues related to noisy load data, an innovative load feature extraction model based on Kullback-Leibler distributionally robust optimization principles is introduced. Then the key features (power boundary/fluctuation features) of each device identified through this robust model are integrated into the constraints of the two-stage NILM model. The two-stage complementary framework includes: the determination of device state interval in the first stage;and the accurate fitting of device power consumption within the device state interval in the second stage. Comparative validation against existing optimization-based models on the AMPds, REFIT, and actual laboratory data sets demonstrate that our proposed model significantly enhances power decomposition accuracy and computational efficiency. In addition, the two-stage complementary framework and load feature extraction model can be applied to other optimization-based models of NILM to improve the computational efficiency of each model and the accuracy of load decomposition. © 2025 IEEE.

关键词： Mixed-integer linear programming

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UniUIR: Considering Underwater Image Restoration as An All-in-One Learner

arXiv

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arXiv 2025年

作者： Zhang, Xu Zhang, Huan Wang, Guoli Zhang, Qian Zhang, Lefei Du, Bo The Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China The Hubei Luojia Laboratory Wuhan China The National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China The School of Information Engineering Guangdong University of Technology Guangzhou510006 China The Horizon Robotics Beijing100083 China

Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degradations that can occur in underwater scenes. To address this limitation, we propose a Universal Underwater Image Restoration method, termed as UniUIR, considering the complex scenario of real-world underwater mixed distortions as an all-in-one manner. To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module. This module enables each expert to identify distinct types of degradation and collaboratively extract task-specific priors while maintaining global feature representation based on linear complexity. Building upon this foundation, to enhance degradation representation and address the task conflicts that arise when handling multiple types of degradation, we introduce the spatial-frequency prior generator. This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts based on image content, thereby improving the accuracy of image restoration. Finally, to more effectively address complex, region-dependent distortions in UIR task, we incorporate depth information derived from a large-scale pre-trained depth prediction model, thereby enabling the network to perceive and leverage depth variations across different image regions to handle localized degradation. Extensive experiments demonstrate that UniUIR can produce more attractive results across qualitative and quantitative comparisons, and shows strong generalization than state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image reconstruction

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On Positive-Unlabeled Classification from Corrupted Data in GANs

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wang, Yunke Xu, Chang Guo, Tianyu Du, Bo Tao, Dacheng Wuhan University School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China The University of Sydney School of Computer Science Faculty of Engineering DarlingtonNSW2008 Australia Peking University Key Laboratory of Machine Perception Shenzhen Graduate School China

This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs and deal with corrupted data. Traditionally, real data are taken as positive while generated data are negative. This positive-negative classification criterion was kept fixed all through the learning process of the discriminator without considering the gradually improved quality of generated data, even if they could be more realistic than real data at times. In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality. The discriminator is thus a classifier for this positive and unlabeled classification problem, and we derive a new Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality the proposed model will achieve and the equivalent optimization goal. Empirically, we find that PUGAN can achieve comparable or even better performance than those sophisticated discriminator stabilization methods. Considering the potential corrupted data problem in real-world scenarios, we further extend our approach to PUGAN-C, which treats real data as unlabeled that accounts for both clean and corrupted instances, and generated data as positive. The samples from generator could be closer to those corrupted data within unlabeled data at first, but within the framework of adversarial training, the generator will be optimized to cheat the discriminator and produce samples that are similar to those clean data. Experimental results on image generation from several corrupted datasets demonstrate the effectiveness and generalization of PUGAN-C. © 1979-2012 IEEE.

关键词： Labeled data

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Extremely Large-scale Array Systems: Near-Field Codebook Design and Performance Analysis

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IEEE Transactions on communications 2025年

作者： Zheng, Feng Yu, Hongkang Wang, Chenchen Sun, Luyang Wu, Qingqing Chen, Yijian Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China

Extremely Large-scale Array (ELAA) promises to deliver ultra-high data rates with increased antenna elements. However, increasing antenna elements leads to a wider realm of near-field, which challenges the traditional design of codebooks. In this paper, we propose novel near-field codebook schemes based on the fitting formula of codewords' quantization performance. First, we analyze the quantization performance properties of uniform linear array (ULA) and uniform planar array (UPA) codewords. Our findings reveal an intriguing property: the correlation formula for ULA codewords can be represented by the elliptic formula, while the correlation formula for UPA codewords can be approximated using the ellipsoid formula. Building on this insight, we propose a ULA uniform codebook that maximizes the minimum correlation based on the derived formula. Moreover, we introduce a ULA dislocation codebook to further reduce quantization overhead. Continuing our exploration, we propose UPA uniform and dislocation codebook schemes. Our investigation demonstrates that oversampling in the angular domain offers distinct advantages, achieving heightened accuracy while minimizing overhead in quantifying near-field channels. Numerical results demonstrate the appealing advantages of the proposed codebook over existing methods in decreasing quantization overhead and increasing quantization accuracy. © 1972-2012 IEEE.

关键词： communication channels (information theory)

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Prevalence of simplex compression in adversarial deep neural networks

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Proceedings of the National Academy of Sciences of the United States of America 2025年第17期122卷

作者： Cao, Yang Chen, Yanbo Liu, Weiwei School of Computer Science Wuhan University Wuhan 430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan 430072 China Institute of Artificial Intelligence Wuhan University Wuhan 430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan 430072 China

Neural collapse (NC) reveals that the last layer of the network can capture data representations, leading to similar outputs for examples within the same class, while outputs for examples from different classes form a simplex equiangular tight frame (ETF) structure. This phenomenon has garnered significant attention due to its implications on the intrinsic properties of neural networks. Interestingly, we observe a simplex compression phenomenon in NC, where the geometric size of the simplex ETF reduces under adversarial training, with the degree of compression increasing as the perturbation radius grows. We provide empirical evidence supporting the existence of simplex compression across a wide range of models and datasets. Furthermore, we establish a rigorous theoretical framework that explains our experimental observations, offering insights into NC under adversarial conditions. Copyright © 2025 the Author(s).

关键词： adversarial training neural collapse neural networks simplex compression

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Toward zero trust in 5G Industrial Internet collaboration systems

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Digital communications and networks 2025年第02期 547-555页

作者： Han Zhang Ziyan Zhang Liquan Chen School of Cyber Science and Engineering Southeast University ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Purple Mountain Laboratories for Network and Communication Security

With the introduction of 5G, users and devices can access the industrial network from anywhere in the world. Therefore, traditional perimeter-based security technologies for industrial networks can no longer work well. To solve this problem, a new security model called Zero Trust(ZT) is desired, which believes in “never trust and always verify”. Every time the asset in the industrial network is accessed, the subject is authenticated and its trustworthiness is assessed. In this way, the asset in industrial network can be well protected, whether the subject is in the internal network or the external network. However, in order to construct the zero trust model in the 5G Industrial Internet collaboration system, there are still many problems to be solved. In this paper, we first introduce the security issues in the 5G Industrial Internet collaboration system, and illustrate the zero trust architecture. Then, we analyze the gap between existing security techniques and the zero trust architecture. Finally, we discuss several potential security techniques that can be used to implement the zero trust model. The purpose of this paper is to point out the further direction for the realization of the Zero Trust Architecture(ZTA) in the 5G Industrial Internet collaboration system.

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