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Deep generative network for image inpainting with gradient semantics and spatial-smooth attention

Journal of Visual Communication and Image Representation 2024年 98卷

作者： Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei Ye, Long School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Applied Laboratory of Dig Data and Education Statistics School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510320 China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China

As a powerful forgery operation in the image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, especially in boundary and high semantic areas. To address this issue, a novel end-to-end network with gradient semantics and spatial-smooth attention (GS-SSA) is proposed, which combines a gradient learning network and an image inpainting network. The gradient learning network is meant to properly anticipate the gradient semantics in the hole region and get a complete gradient semantic map. The image inpainting network utilizes the complete gradient semantic map to better repair the missing pixels and obtain the final inpainting results. Moreover, spatial-smooth attention is introduced into the image inpainting network, considering both the spatial structural relations and the surrounding information in the hole region. Experimental results on public datasets show the superiority of the proposed method, especially in large-hole region tasks. © 2023 Elsevier Inc.

关键词： Semantics

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Bone Layer Perception in Milling Process Based on Video Sequence Images during Robot-assisted Laminectomy

Bone Layer Perception in Milling Process Based on Video Sequ...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Li, Haiyang Li, Meng Qi, Xiaozhi Yang, Yuanyuan Hu, Ying Shenyang Aerospace University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Harbin Institute of Technology China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665481090

To optimize the operation ability of the autonomous robot under the background of complex surgery, this paper extracted the state features of milling video sequence images to realize bone layer perception and improve the perception ability of the spinal robot. The bone layer sensing algorithm mainly consists of four parts: improved moving object detection algorithm, moving object tracking algorithm, state feature extraction algorithm and bone layer recognition algorithm. Aiming at the shortcomings of the existing moving target detection methods, this paper proposes an improved moving target detection algorithm based on the existing Background Subtractor MOG algorithm, which is suitable for milling video sequences. The grinding machine state obtained by the improved algorithm is used as the input of the moving target tracking algorithm, and the kernel correlation filter (KCF) is used to realize the target tracking. According to the tracking of the moving target, the state features of the milling area are extracted and the bone layer is identified, so as to realize the bone layer perception in the milling process. © 2022 IEEE.

关键词： Object detection

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CP-UNet: Contour-based Probabilistic Model for Medical Ultrasound Images Segmentation

arXiv

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arXiv 2024年

作者： Yu, Ruiguo Zhang, Yiyang Tian, Yuan Liu, Zhiqiang Li, Xuewei Gao, Jie College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China School of Future Technology Tianjin University Tianjin300350 China

Deep learning-based segmentation methods are widely utilized for detecting lesions in ultrasound images. Throughout the imaging procedure, the attenuation and scattering of ultrasound waves cause contour blurring and the formation of artifacts, limiting the clarity of the acquired ultrasound images. To overcome this challenge, we propose a contour-based probabilistic segmentation model CP-UNet, which guides the segmentation network to enhance its focus on contour during decoding. We design a novel down-sampling module to enable the contour probability distribution modeling and encoding stages to acquire global-local features. Furthermore, the Gaussian Mixture Model utilizes optimized features to model the contour distribution, capturing the uncertainty of lesion boundaries. Extensive experiments with several state-of-the-art deep learning segmentation methods on three ultrasound image datasets show that our method performs better on breast and thyroid lesions segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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Releasing Inequality Phenomena in L∞-Adversarial Training via Input Gradient Distillation

arXiv

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arXiv 2023年

作者： Chen, Junxi Dong, Junhao Xie, Xiaohua School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

Since adversarial examples appeared and showed the catastrophic degradation they brought to DNN, many adversarial defense methods have been devised, among which adversarial training is considered the most effective. However, a recent work showed the inequality phenomena in l∞-adversarial training and revealed that the l∞-adversarially trained model is vulnerable when a few important pixels are perturbed by i.i.d. noise or occluded. In this paper, we propose a simple yet effective method called Input Gradient Distillation (IGD) to release the inequality phenomena in l∞-adversarial training. Experiments show that while preserving the model’s adversarial robustness, compared to PGDAT, IGD decreases the l∞adversarially trained model’s error rate to inductive noise and inductive occlusion by up to 60% and 16.53%, and to noisy images in Imagenet-C by up to 21.11%. Moreover, we formally explain why the equality of the model’s saliency map can improve such robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model for C/C++ Code

VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model ...

引用

International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Zhe Xiong Weiyu Dong School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Recently, deep learning techniques have received widespread attention in code vulnerability and vulnerability detection. However, there are some problems with current vulnerability detection techniques, firstly, because of the non-homogeneity of vulnerability categories and quantities, there are omission false positives and overfitting problems for vulnerabilities with a small amount of data. Second, the existing detection methods have limited ability to characterize the code, there is insufficient depth of characterization, does not contain all of its vulnerability characteristics or is interspersed with useless information. Finally, the current research does not use the powerful feature mining capability of pre-trained models and does not employ model fusion to increase the effectiveness of model vulnerability detection. To solve the above problems, this paper proposes a new deep learning based pre-trained model for code vulnerability detection tool VulD-CodeBERT. By fine-tuning CodeBERT, a code adaptation extension for pre-trained models developed by Microsoft, the CodeBERT model can solve the problem of not being able to learn the vulnerability patterns due to insufficient data, thus resulting in missed false alarms. The sequence output from the CodeBERT model are added to a BiLSTM network with an attention mechanism to mine the local key features of the vulnerabilities combined with the global features of the pooler output from the CodeBERT model to deeply mine the dependencies between the vulnerability statements and derive the probability of the existence of the vulnerability. For vulnerability types that are difficult to uncover captured features or the number of samples is not enough, a polynomial plain Bayesian classifier is used for detection, and finally the two models are combined to deeply mine the vulnerability features, thus solving the problem of difficult and small samples, and reducing the leakage rate and false alarm rate. Experimental results sh

关键词： Deep learning Adaptation models Codes Attention mechanisms Computational modeling Feature extraction Polynomials

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APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging

arXiv

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arXiv 2023年

作者： Chen, Zixuan Yang, Lingxiao Lai, Jianhuang Xie, Xiaohua The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse problem in imaging that aims to acquire high-quality CT images based on sparsely-sampled measurements. Recent works use Implicit Neural Representations (INRs) to build the coordinate-based mapping between sinograms and CT images. However, these methods have not considered the correlation between adjacent projection views, resulting in aliasing artifacts on SV sinograms. To address this issue, we propose a self-supervised SVCT reconstruction method – Anti-Aliasing Projection Representation Field (APRF), which can build the continuous representation between adjacent projection views via the spatial constraints. Specifically, APRF only needs SV sinograms for training, which first employs a line-segment sampling module to estimate the distribution of projection views in a local region, and then synthesizes the corresponding sinogram values using a center-based line integral module. After training APRF on a single SV sinogram itself, it can synthesize the corresponding dense-view (DV) sinogram with consistent continuity. High-quality CT images can be obtained by applying re-projection techniques on the predicted DV sinograms. Extensive experiments on CT images demonstrate that APRF outperforms state-of-the-art methods, yielding more accurate details and fewer artifacts. Our code will be publicly available soon. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

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DFPC-SLAM:A Dynamic Feature Point Constraints-Based SLAM Using Stereo Vision for Dynamic Environment

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Guidance, Navigation and Control 2023年第1期3卷 46-60页

作者： Bo Zeng Chengqun Song Cheng Jun Yuhang Kang CAS Key Laboratory of Human-Machine Intelligence-Synergy System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P.R.China University of Chinese Academy of Sciences Beijing P.R.China The Chinese University of Hong Kong Hong Kong P.R.China

Visual SLAM methods usually presuppose that the scene is static, so the SLAM algorithm formobile robots in dynamic scenes often results in a signicant decrease in accuracy due to thein°uence of dynamic objects. In this paper, feature points are divided into dynamic and staticfrom semantic information and multi-view geometry information, and then static region featurepoints are added to the pose-optimization, and static scene maps are established for dynamicscenes. Finally, experiments are conducted in dynamic scenes using the KITTI dataset, and theresults show that the proposed algorithm has higher accuracy in highly dynamic scenes comparedto the visual SLAM baseline.

关键词： SLAM stereo vision semantic segmentation multi-view geometry dynamic scenes.

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Variation of sEMG with Muscle Fatigue for Lower Limb Stroke Patient During Walking

Variation of sEMG with Muscle Fatigue for Lower Limb Stroke ...

引用

2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Shang, Wenze Huang, Ruisen Liao, Wei-Hsin Wu, Xinyu Gao, Fei Southern University of Science and Technology Shenzhen518005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong999077 Hong Kong

ISBN: (纸本)9781665481090

SEMG signal is widely used and explored in control strategies of powered assistive human-robot interaction systems due to its non-invasive nature and ability to estimate motion intention well. However, prolonged use of the sEMG-based systems leads to muscle fatigue, contributing to unstable sEMG signals and regarding the performance of sEMG-based control strategies. In this study, three subjects, including two healthy subjects and one stroke subject, participated in our experiment, and the time domain, frequency domain, and complex features extracted from their sEMG signals were utilized to evaluate muscle fatigue. The results indicate that sEMG from the stroke side of the stroke subject contains more non-linear and chaotic parts, and the statistical differences between different trials are more significant, indicating that stroke has a more significant impact on the stroke side of the stroke subject. This study suggests that RMS in the stroke side of stroke subjects is less than that in others, whereas FuzzyEn in the stroke side of stroke subjects is greater than that in others, indicating that stroke leads to different motion patterns and more susceptibility to muscle fatigue. © 2024 IEEE.

关键词： Human robot interaction

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Non-Markovian effect enhanced quantum noises in a coherent Ising machine

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Optics Express 2025年第11期33卷 22208-22221页

作者： Chen-Rui Fan Bo Lu Yi-Xuan Yao Qing Ai Chuan Wang School of Artificial Intelligence Beijing Normal University Beijing 100875 China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou 450001 China lubo@*** School of Physics and Astronomy Applied Optics Beijing Area Major Laboratory Beijing 100875 China Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing Normal University Beijing 100875 China wangchuan@***

Combinatorial optimization problems (COPs) constitute a fundamental class of computational challenges with extensive applications across scientific and industrial domains. The emergence of the coherent Ising machine (CIM) as a computational paradigm has demonstrated exceptional capabilities in rapidly generating arbitrary spin configurations and effectively solving large-scale COPs. Despite its promising potential, the CIM framework encounters an inherent limitation related to amplitude heterogeneity. Current approaches to this limitation primarily rely on Gaussian approximations to model quantum noise in the system’s amplitude dynamics within the original CIM framework. However, these methods generally fail to account for the temporal and periodic non-Markovian characteristics of pulses in practical environments. In this study, we introduce temporally correlated non-Markovian noise into the dynamics of both conventional and spiking neural network-based CIM systems, investigating their performance through comprehensive hyperparameter analysis. Our method incorporates a detailed examination of hyperparameter selection and its influence on algorithmic efficiency. Through extensive numerical simulations, we demonstrate that the integration of non-Markovian noise significantly accelerates variance evolution in both canonical coordinates and amplitude dynamics over time. The proposed approach not only exhibits enhanced computational performance but also shows superior scalability for large-scale problem instances. These findings suggest that our approach offers distinct advantages in addressing complex COPs, potentially opening new avenues for efficient optimization in various applications.

关键词： Neural networks Nonlinear optical fibers Numerical simulation Optical parametric oscillators Optical systems Quantum noise

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SPAFuzz: Web Security Fuzz Testing Tool Based on Fuzz Testing and Genetic Algorithm

SPAFuzz: Web Security Fuzz Testing Tool Based on Fuzz Testin...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Jiang Lin Weiyu Dong School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China

Fuzzing is one of the most effective methods currently available for detecting web vulnerabilities. However, traditional fuzzing suffers from issues such as low coverage and limited diversity in test cases. To address these issues, we propose a way to generate test cases based on dynamic feature libraries and attack models by analysing the characteristics of SQL and XSS. Additionally, we introduce a genetic algorithm to guide the population's evolution by monitoring the state changes of the web application. Furthermore, we improved the selection phase of the genetic algorithm to optimally deform multiple iterations for use cases with low fitness values in order to preserve the population diversity to expand the attack capability of the test cases and based on this the SPAFuzz fuzzy testing tool was implemented. Experimental results show that SPAFuzz is able to detect more network vulnerabilities than Sqlmap and Powerfuzzer.

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