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Image Inpainting Detection Based on High-Pass Filter Attention Network

Computer Systems Science & Engineering 2022年第12期43卷 1145-1154页

作者： Can Xiao Feng Li Dengyong Zhang Pu Huang Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409USA

Image inpainting based on deep learning has been greatly *** original purpose of image inpainting was to repair some broken photos, suchas inpainting artifacts. However, it may also be used for malicious operations,such as destroying evidence. Therefore, detection and localization of imageinpainting operations are essential. Recent research shows that high-pass filteringfull convolutional network (HPFCN) is applied to image inpainting detection andachieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, weintroduce the squeezed excitation blocks (SE) and propose a high-pass filter attention full convolutional network (HPACN). In feature extraction, we apply concurrent spatial and channel attention (scSE) to enhance feature extraction and obtainmore information. Channel attention (cSE) is introduced in upsampling toenhance detection and localization. The experimental results show that the proposed method can achieve improvement on ImageNet.

关键词： Image inpainting detection spatial attention channel attention full convolutional network high-pass filter

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Improved RUL prediction method of wind turbine gearbox bearings under strong noises

Improved RUL prediction method of wind turbine gearbox beari...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Sheng Xiang Penghua Li Jie Hou Mamadsho Ilolov Jiwei Wang Deqi Huang Lirong Xie School of Automation& Key Laboratory of Intelligent Computing for Big Data Chongqing University of Posts and Telecommunications Chongqing China Center for Innovative Development of Science and New Technologies National Academy of Sciences of Tajikistan Dushanbe Tajikistan School of Electrical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

As essential components within the wind turbine gearbox, bearings are critical, and forecasting their Remaining Useful Life (RUL) is paramount. To enhance the integration of current inputs with historical data in Recurrent Neural Network (RNN) models, the Mogrifier Long Short-Term Memory (LSTM) approach has been devised. It interacts with inputs and recurrent data in a predetermined fashion, marking its initial application in wind turbine gearbox bearing RUL prediction. Furthermore, the Relative Standard Deviation of Envelope Spectrum (RSDES), a reliable health indicator (HI) under high noise conditions, is introduced for precise evaluation of the gearbox bearing health. Consequently, a novel RUL prediction technique is introduced. Initially, RSDES from vibration data serves as HIs. Subsequently, utilizing these HIs, the Mogrifier LSTM predicts future HIs, and the bearing’s RUL is determined once the predicted HI surpasses the failure threshold. Empirical outcomes underscore the proposed method’s superiority over existing techniques in bearing RUL prediction.

关键词： Vibrations Recurrent neural networks Accuracy Noise data models Wind turbines Reliability

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Particle-Assisted Deep Reinforcement Learning for Quantum State Manipulation

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IEEE Transactions on Evolutionary Computation 2025年

作者： Yu, Haixu Liu, Xiang Wang, Bohui Zhao, Xudong Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China Dongguan University of Technology Institute of Science and Technology Innovation Dongguan523000 China Talent Big Data Intelligent Analysis and Evaluation Key Laboratory of MIIT Beijing100804 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

Applying deep reinforcement learning (DRL) to solve quantum control problems has become a popular research direction. However, the exploration capability and reward design for the learning agent, which usually affect the DRL's application performance, has not been sufficiently emphasized. In this article, we propose a particle-assisted DRL (PDRL) method to address the above concern by enhancing exploration capabilities and designing appropriate reward functions for efficient quantum state manipulation. In PDRL, each episode in the quantum learning process is characterized by three kinds of events, i.e., unidentifiable, identifiable, and successful events. To improve exploration, exploration particles and feedback particles are employed in the early learning phase when episodes end in identifiable and successful events, respectively. To assign rewards, three event-based reward functions are provided for the DRL's agent, exploration particles and feedback particles, respectively. Numerical results on single-qubit, two-qubit, and many-qubit systems validate the effectiveness of PDRL. Comparative results with existing DRL methods demonstrate the superior performance of PDRL for quantum state manipulation. © 1997-2012 IEEE.

关键词： Deep reinforcement learning

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SPA: a graph spectral alignment perspective for domain adaptation 23

SPA: a graph spectral alignment perspective for domain adapt...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhiqing Xiao Haobo Wang Ying Jin Lei Feng Gang Chen Fei Huang Junbo Zhao College of Computer Science and Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University CUHK-SenseTime Joint Lab The Chinese University of Hong Kong School of Computer Science and Engineering Nanyang Technological University Alibaba Group

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a finegrained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA.

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Learning Common and Label-Specific Features for Label Distribution Learning with Correlation Information

SSRN

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SSRN 2024年

作者： Wu, Yilin Lin, Yaojin Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou363000 China

Label distribution learning quantifies the label space for each instance and has wide applicability in different fields. However, most existing work primarily focuses on common features, ignores the significance of label-specific features. Meanwhile, label correlation is well used in these work, but they still have limitations in capturing instance correlation. Therefore, in this paper, we propose a novel approach for label distribution learning to learn common and label-specific features with correlation information i.e. instance correlation and label correlation. First, we conduct $\ell_{2,1}-$norm regularization to select common and label-specific features, simultaneously. Then, we implement an optimization function to identify instance correlation in feature space. In addition, label correlation is collected with Pearson correlation coefficient. Finally, we design an objective function that enables the joint learning of common and label-specific features while leveraging correlation information. Comprehensive experiments manifest the superiority of our proposed approach against other well-established label distribution learning algorithms. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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An Object-Deputy Approach For Chinese Genealogy Collaborative Management 25

An Object-Deputy Approach For Chinese Genealogy Collaborativ...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Jiang, Huan Liu, Honghao Li, Rongrong Peng, Yuwei Xu, Bin Peng, Zhiyong School of Computer Science Wuhan University Wuhan430072 China School of History Wuhan University Wuhan430072 China Big Data Institute Wuhan University Wuhan430072 China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan430072 China

ISBN: (纸本)9781665405270

Chinese genealogy management systems support many functions, such as making genealogy, retrieval and so on. Due to the lack of collaborative management of genealogical data, few systems support genealogical analysis, especially event analysis. In this paper, we propose an approach to manage the Chinese genealogy collaboratively. At first, we analyze the semantics of entities in genealogy. Then we introduce the Object-Deputy mechanism to model Chinese genealogy and implement it in Totem database. Based on this model, we study the common queries and general event relation inference. Additionally, we propose a deep learning model integrating Object Deputy semantics for genealogical event reasoning. Experiments show that our method requires less storage space and achieves better query performance compared with relational method or graph method. Especially, our model integrating the Object-Deputy semantics promotes the accuracy and recall for event reasoning. It proves the effectiveness of our approach. © 2022 IEEE.

关键词： Semantics

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GESH-NET: GRAPH-ENHANCED SPHERICAL HARMONIC CONVOLUTIONAL NETWORKS FOR CORTICAL SURFACE REGISTRATION

arXiv

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arXiv 2024年

作者： Zhang, Ruoyu Wang, Lihui Tang, Kun Xu, Jingwen Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical or functional regions of different individuals, which is of great importance for neuroimaging studies across different populations. Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical registration methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. With the spectacular success of deep learning in the field of computer vision, researching cortical surface image registration techniques based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. The results show that the proposed network outperforms the other deep learning-based registration m

关键词： Spheres

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SS4Rec: Continuous-Time Sequential Recommendation with State Space Models

arXiv

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arXiv 2025年

作者： Xiao, Wei Wang, Huiying Zhou, Qifeng Department of Automation Xiamen University Xiamen361005 China School of Engineering Westlake University Hangzhou China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Xiamen University Xiamen China

Sequential recommendation is a key area in the field of recommender systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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