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Learning Video Salient Object Detection Progressively From Unlabeled Videos

IEEE Transactions on Multimedia 2025年 27卷 2423-2435页

作者： Xu, Binwei Jiang, Qiuping Liang, Haoran Zhang, Dingwen Liang, Ronghua Chen, Peng Ningbo University Faculty of Information Science and Engineering Ningbo315211 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710072 China

Recently, deep learning-based video salient object detection (VSOD) has achieved some breakthroughs, but these methods rely on expensive annotated videos with pixel-wise annotations or weak annotations. In this paper, based on the similarities and differences between VSOD and image salient object detection (SOD), we propose a novel VSOD method via a progressive framework that locates and segments salient objects in sequence without utilizing any video annotation. To efficiently use the knowledge learned in the SOD dataset for VSOD efficiently, we introduce dynamic saliency to compensate for the lack of motion information of SOD during the locating process while maintaining the same fine segmenting process. Specifically, we utilize the coarse locating model trained on the image dataset, to identify frames with both static and dynamic saliency. Locating results of these frames are selected as spatiotemporal location labels. Moreover, by tracking salient objects in adjacent frames, the number of spatiotemporal location labels is increased. On the basis of these location labels, a two-stream locating network with an optical flow branch is proposed to capture salient objects in videos. The results with respect to five public benchmarks demonstrate that our method outperforms the state-of-the-art weakly and unsupervised methods. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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A Novel Sleep Modulation and Quality Classification Strategy Based EEG Signals and Multi-Scale Attention Deep Learning

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IEEE Internet of Things Journal 2025年第11期12卷 17377-17386页

作者： Lou, Yajun Zhou, Lu Song, Fanlei Xu, Fanyao Wang, Xuejing Li, Weitao Qian, Zhiyu Nanjing University of Aeronautics and Astronautics Key Laboratory of Multimodal Brain-Computer Precision Drive Ministry of Industry and Information Technology the Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Department of Biomedical Engineering College of Automation Engineering Nanjing210016 China

Sleep disorders have become the second most prevalent mental health condition globally, posing a significant challenge to public health. Consequently, there is an imperative need for the development of effective, non-pharmacological interventions to enhance sleep quality. In this study, a novel sleep modulation technique utilizing transcutaneous electrical nerve stimulation (TENS) is introduced. A specialized stimulation mode is designed by integrating sine waves of varying frequencies, including spindle waveform components. Additionally, to facilitate an accurate evaluation of sleep quality and enable individualized interventions, a multi-scale attention-based deep learning model (MSA-Net) is proposed for sleep quality classification. It consists of a two-branch convolution block (TBC-block), an adaptive feature recalibration (AFR) module, three dense blocks, three attention transition blocks. By extracting the deep features of the electroencephalography (EEG) raw data, the sleep quality classification is realized. Experimental results indicate that the proposed stimulation effectively enhances and stabilizes slow-wave activity, significantly extending the duration of deep sleep and enhancing overall sleep stability. In addition, the MSA-Net demonstrates robust performance, achieving exceptional performance with an overall accuracy of 0.881, F1 score of 0.903, and Kappa score of 0.845, outperforming baseline models across all evaluation metrics. These results demonstrate the potential of combining TENS-based sleep modulation with deep learning techniques, offering an effective framework for enhancing sleep quality and advancing personalized sleep modulation strategies. © 2025 IEEE.

关键词： Sleep research

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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A robust pointer meter reading method for inspection robots in real industrial scenarios

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Neural Computing and Applications 2025年第7期37卷 5369-5379页

作者： Huang, Zhiqing Wang, Yuchao Zhang, Yanxin Zhang, Chenguang College of Computer Science Beijing University of Technology Beijing100124 China School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Beijing Elitenect Technologies Co. Ltd. Beijing100085 China

In real industrial scenarios, inspection robots can replace humans to automatically read pointer meters, which greatly improves productivity and safety. However, existing automatic reading methods perform poorly in complex robot operating environments. To this end, we propose an automatic reading method for pointer meters, which can be better applied to inspection robot working conditions. Firstly, we propose a meter detection network, Yolo_Meter, which combines an attention mechanism and an adaptive feature fusion module. This network can accurately locate the meter from the perspective of robots and crop out images that are suitable for automatic meter readings. Secondly, we propose an oriented pointer detection network (OPDNet) to fit the tip position of the pointer precisely. Thirdly, we design a deep neural network OCR_Meter to obtain the scale and unit information of the meter by text detection and a filtering algorithm, which is adaptable to multiple types of meters. Finally, we propose a polar pixel method for locating the main scale lines and design the local angle method to calculate the readings of the pointer meters. Adequate experiments demonstrate the high accuracy and robustness of our method in real-world scenarios, with an average global error of only 0.73%. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep neural networks

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in Engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Control Structures and Algorithms for Force Feedback Bilateral Teleoperation Systems: A Comprehensive Review

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computer Modeling in Engineering & Sciences 2025年第2期142卷 973-1019页

作者： Jiawei Tian Yu Zhou Lirong Yin Salman A.AlQahtani Minyi Tang Siyu Lu Ruiyang Wang Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Force feedback bilateral teleoperation represents a pivotal advancement in control technology,finding widespread application in hazardous material transportation,perilous environments,space and deep-sea exploration,and healthcare *** paper traces the evolutionary trajectory of force feedback bilateral teleoperation from its conceptual inception to its current *** elucidates the fundamental principles underpinning interaction forces and tactile exchanges,with a specific emphasis on the crucial role of tactile *** this review,a quantitative analysis of force feedback bilateral teleoperation development trends from 2011 to 2024 has been conducted,utilizing published journal article data as the primary source of *** review accentuates classical control frameworks and algorithms,while also delving into existing research advancements and prospec-tive breakthrough ***,it explores specific practical scenarios ranging from intricate surgeries to hazardous environment exploration,underscoring the technology’s potential to revolutionize industries by augmenting human manipulation of remote *** underscores the pivotal role of force feedback bilateral teleoperation as a transformative human-machine interface,capable of shaping flexible control strategies and addressing technological *** research endeavors in force feedback bilateral teleoperation are expected to prioritize the creation of more immersive experiences,overcoming technical hurdles,fortifying human-machine collaboration,and broadening application domains,particularly within the realms of medical intervention and hazardous *** the continuous progression of technology,the integration of human intelligence and robotic capabilities is expected to produce more innovations and breakthroughs in the field of automatic control.

关键词： Force feedback bilateral teleoperation robot development history application prospect

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IPT-ILR: Image Pyramid Transformer Coupled With Information Loss Regularization for All-in-One Image Restoration

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4341-4356页

作者： Yang, Sai Hu, Bin Liu, Fan Wu, Xiaoxin Ding, Weiping Zhou, Jun Nantong University School of Electrical Engineering and Automation Nantong226019 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China Hohai University College of Computer Science and Software Engineering Nanjing210098 China Griffith University School of Information and Communication Technology NathanQLD4111 Australia

All-in-one image restoration has recently developed to be a new research trend in the low-level computer vision field, aiming to tackle multiple image degradation types simultaneously in a unified model. As a typical multi-task learning, existing approaches focus on modeling either the specificity or commonality among different image restoration tasks. To exploit the unique strengths of both worlds, we propose a method of Image Pyramid Transformer coupled with Information Loss Regularization (IPT-ILR), in which the multi-scale architecture structure can excavate more information for multiple restoration tasks concurrently, while the learning strategy can identify the difference among multiple restoration tasks depending on the degree of information loss in each restoration task. Specifically, it first establishes a new Image Pyramid Transformer Network (IPT-Network) to accommodate multiple image restoration tasks. Given original degraded images, the IPT-Network exploits the image pyramid technique to establish a series of images with different scales, which are then restored by transformer-like auto-encoders. Moreover, the restored image on a low-level scale is referenced to assist restoring the degraded image on a high-level scale. Next, Information Loss Regularization (ILR) is presented to optimize the IPT-Network. ILR calculates the average distance between degraded images and their clean counterparts as the weights, which automatically implement different penalties for different image restoration tasks, thus avoiding the short-cut phenomenon for the easy task while encouraging the hard task. Extensive experiments have been conducted with 6 image restoration tasks in the all-in-one setting. The results show our method performs favorably against numerous state-of-the-art methods across most tasks, including image denoising, image deblurring, image dehazing, image deraining, image desnowing, as well as low-light enhancement. © 1991-2012 IEEE.

关键词： Restoration

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Performance Enhancement for Non-Light-of-Sight Optical Camera Communication using RPSNN

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IEEE Photonics technology Letters 2025年第14期37卷 789-792页

作者： Shi, Jin Luo, Jing He, Jing Wuhan University of Technology School of Information Engineering Wuhan430070 China Wuhan University of Technology School of Automation Wuhan430070 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Non-line-of-sight (NLOS) optical cameracommunication (OCC) offers a promising avenue for improvinglink availability, thanks to its resilience against link ***, the diverse reflection coefficients across variousreflective points introduce background interference andinter-symbol interference (ISI). Thus, the pixel values in onespecific column may suffer severe fluctuation, complicating signaldemodulation. To mitigate the background interference andimprove the transmission performance in NLOS OCC system, inthis paper, row pixel sorting based neural network (RPSNN) isproposed and experimentally demonstrated. The results indicatethat with a white and heterogeneous background under reflectiveilluminance of 70 lux, the RPSNN scheme attains a data rate of8.16 kbps with an average bit error rate (BER) of 1.4×10-3 and5.4×10-3. In contrast, the conventional artificial neural network(ANN) without row pixel sorting only achieves a BER of 2.3×10-2in white background and even worse in heterogeneousbackground. These results prove that the proposed RPSNNscheme is a promising scheme for OCC system in NLOS scenarios. © 1989-2012 IEEE.

关键词： Optical communication

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