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IEEE information technology and Mechatronics Engineering Conference (ITOEC)

作者： Xiaodong Su Hongyan Xu Jiayuan Zhao Fengchun Zhang Xu Chen College of Computer and Information Engineering Harbin University of Commerce Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China

Convolutional neural networks have been widely used for human pose estimation tasks, but with some issues. It is limited to local receptive fields and it is difficult to capture global information. To address this problem, we propose the GM-HRNet network. The network aggregates the multi-stage feature information of HRNet, and makes full use of the criss-cross attention and channel attention to obtain the context information in the space and channel dimensions, and realizes the modeling of the global relationship, so as to effectively locate the keypoints of the human body. In this paper, HRNet is used as a benchmark to conduct experiments on MPII dataset. Experimental results show that the accuracy of GM-HRNet is 1.3 percent higher than that of HRNet under the same experimental conditions, which validates the effectiveness of the GM-HRNet model.

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Diabetic Retinopathy Grading with Weakly-Supervised Lesion Priors 48

Diabetic Retinopathy Grading with Weakly-Supervised Lesion P...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Hou, Junlin Xiao, Fan Xu, Jilan Feng, Rui Zhang, Yuejie Zou, Haidong Lu, Lina Xue, Wenwen Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Fudan University Academy for Engineering and Technology Shanghai China Shanghai Eye Diseases Prevention & Treatment Center Shanghai China

ISBN: (纸本)9781728163277

Explicit information of lesions can provide visual instructions for diabetic retinopathy (DR) grading on fundus images. However, pixel-level lesion annotations are extremely difficult and time-consuming to acquire. In this work, we propose a novel weakly-supervised lesion-aware network for DR grading, which enhances the discriminative features with lesion priors by only image-level supervision. Specifically, we design a lesion attention module that generates lesion activation maps by introducing an auxiliary task of binary DR identification. Lesion activation maps are utilized to assist the network to focus on the most relevant regions for boosting DR grading performance. Besides, we particularly devise an adaptive joint loss to balance the DR identification and DR grading tasks dynamically. Extensive results on the public DR dataset demonstrate the superiority and generality of our proposed lesion-aware network. The interpretability of generated lesion activation maps is also verified by the comparison with ground truth segmentation masks. © 2023 IEEE.

关键词： Activation map Diabetic retinopathy grading Fundus image Lesion priors Weakly-supervised

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Time Series Anomaly Detection Based on Normalized Flow and Bayesian Networks 9

Time Series Anomaly Detection Based on Normalized Flow and B...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yao, Honglei Liu, Donglan Chang, Yingxian Liu, Xin Tang, Chaofan State Grid Shandong Electric Power Research Institute Jinan China State Grid Shandong Electric Power Company Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Time series anomaly detection often faces significant challenges due to the high dimensionality, interdependence, and label sparsity of the data. Traditional methods struggle to model the intricate relationships between multiple time series components and often fail to accurately detect anomalies due to insufficient data dependence mining. To address these issues, we introduce GCRNF, a novel Graph-Enhanced Standardized Flow Model that incorporates a Bayesian network for causality modeling. This model decomposes joint probabilities into conditional probabilities and utilizes a graph dependency encoder with hybrid CNN-RNN models and Graph Convolutional Networks (GCNs). Our experimental results demonstrate that GCRNF outperforms existing methods in density estimation, anomaly detection, and drift identification within time series distributions. Specifically, GCRNF showed a significant improvement in detection accuracy and robustness compared to traditional methods, providing a more effective solution for identifying anomalies in complex multivariate time series data. © 2024 IEEE.

关键词： Markov processes

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Multifunctional metasurface based on vanadium dioxide in the terahertz frequency

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Applied Optics 2025年第17期64卷 4692-4700页

作者： Zou Long Zhengji Xu School of Microelectronics Science and Technology Sun Yat-Sen University Zhuhai 519000 China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-Sen University Zhuhai 519000 China

As metasurface technology has been developing rapidly over the past decades, multi-multiplexing and tunability are evolving into hot spots in its development. Here, a metasurface fit for vortex beam generation, beam focusing, linear-to-circular polarization conversion, and absorption is proposed in this study, contingent upon the phase change properties of vanadium dioxide. When the temperature is 25°C, the vortex beam and focusing generated when the circularly polarized wave is incident on the metasurface, the calculated outcomes demonstrate that the cross-polarization reflective coefficient is close to the co-polarization coefficient at the frequency range of 0.5–0.9 THz and 1.2–1.5 THz, implementing the linear-to-circular polarization conversion. When heated up to 68°C, it functions as an absorber at 1.55 THz when the y-polarized wave is incident vertically on the metasurface. The designed metasurface is likely to be used in the fields of terahertz communication and imaging systems.

关键词： Bandpass filters Circular polarization Imaging systems Polarization Surface plasmon polaritons Terahertz imaging

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Improving Automatic Piano Transcription by Refined Feature Fusion and Weighted Loss 9th

Improving Automatic Piano Transcription by Refined Feature ...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Zhao, Jiahao Wu, Yulun Wen, Liang Ma, Lianhang Ruan, Linping Wang, Wantao Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China CETHIK Group Co. Ltd Hangzhou311199 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Automatic Piano Transcription is to transcribe raw audio files into annotated piano rolls. In recent studies, jointly estimating pitch, onset, offset, and velocity of each note is commonly used. The previous state-of-the-art "Onsets and Frames" model chooses to concatenate the output of both onset and offset sub-tasks with extracted features to improve the frame-wise pitch detection, which is, however, low-efficiency in our opinion. In this paper, we proposed an improved piano transcription model by doing feature fusion and loss weighting. Our proposed model outperforms the baselines by a large margin. It also shows comparable performance with the state-of-the-art "High-Res PT" model in note metrics and outperforms it in frame metrics with an F1 score of 90.27%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Musical instruments

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Steel Surface Defect Detection Based on Improved YOLOv8 20

Steel Surface Defect Detection Based on Improved YOLOv8

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Zhang, Jichen Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

There are some defects on the surface of steel that are difficult to detect, so we propose an improved algorithm based on YOLOv8 for detecting defects that are difficult to be detected. This improvement includes the introduction of an SPD module to minimize the problem of information loss due to downsampling;the addition of a small object detection head combined with EMA attention to further enhance the detection capabilities for small targets;the use of BiFPN to replace the original PANet, applying different weights to different features to strengthen feature fusion. Experiments were conducted on the NEU-DET dataset, and the algorithm improved mAP to 80.2%, an improvement of 3.9% over the baseline model. © 2024 IEEE.

关键词： Change detection

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Distributed online bandit tracking for Nash equilibrium under partial-decision information setting

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Science China(Technological Sciences) 2023年第11期66卷 3129-3138页

作者： FENG ZhangCheng XU WenYing CAO JinDe YANG ShaoFu RUTKOWSKI Leszek Jiangsu Provincial Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 211189China Frontiers Science Center for Mobile Information Communication and Security Nanjing 210096China School of Computer Science and Engineering Southeast UniversityNanjing 211189China The Systems Research Institute of the Polish Academy of Sciences Warsaw 01-447Poland AGH University of Science and Technology Krakow 30-059Poland

This paper is concerned with a Nash equilibrium(NE)tracking issue in online games with bandit feedback,where cost functions vary with time and agents only have access to the values of these functions at two points during each round.A partial-decision information setting is considered,in which agents have only access to the decisions of their *** primary objective of this paper is to develop a distributed online NE tracking algorithm that ensures sublinear growth of regret with respect to the total round T,under both the bandit feedback and partial-decision information *** utilizing a two-point estimator together with the leader-following consensus method,a new distributed online NE tracking algorithm is established with the estimated gradient and local estimated decisions based on the projection gradient-descent ***,sufficient conditions are derived to guarantee an improved upper bound of dynamic regret compared to existing bandit ***,a simulation example is presented to demonstrate the effectiveness of the proposed algorithm.

关键词： online game bandit feedback partial-decision two-point gradient estimator

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FGCM: Modality-behavior Fusion Model Integrated with Graph Contrastive Learning for Multimodal Recommendation

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IEEE Multimedia 2025年

作者： Mo, Feng Xiao, Lin Song, Qiya Gao, Xieping Song, Wenzhuo Wang, Shoujin Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China Northeast Normal University Changchun China University of Technology Sydney Sydney Australia

Multimodal recommender systems (MRSs) aim to integrate information from multiple modalities, for better capturing users' preferences. However, existing MRSs usually face the challenge of data sparsity, especially for the limited user-item interactions. It is not uncommon that a user interacts with only a few items out of millions. More importantly, the interaction information is easy to be overwhelmed, leading to poor recommendation performance. To tackle these significant gaps for further improving the performance of MRSs, we propose a Modality-behavior Fusion Model Integrated with Graph Contrastive Learning for Multimodal Recommendation (FGCM). First, through the introduction of random noise into graph contrastive learning, FGCM adeptly mitigates data sparsity. Then, a modality-behavior fuser is proposed to effectively fuse interaction data and multimodal side information data by taking cooperative signals and multimodal signals as the guidance. Extensive experiments on three datasets demonstrate that our proposed approach outperforms several state-of-the-art methods. © 2025 IEEE.

关键词： Contrastive Learning

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SPRGAN: Streamlined Progressive Refinement for Adversarial Point Cloud Video Upsampling

SPRGAN: Streamlined Progressive Refinement for Adversarial P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Zhiyong Liu, Ruyu Wang, Chaochao Zhang, Xianchao Zhang, Jianhua Liu, Xiufeng School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Zhejiang China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark

ISBN: (纸本)9798350368741

Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue, this paper presents a self-supervised adversarial upsampling method for point cloud video streams called SPR-GAN. In the generator, we design the Temporal Iterative Graph module to learn local features for each frame and captures long-range spatial information using three iterations of graph convolution operations. Then the Contextual Temporal Fusion module is developed to merge information between different frames, synthesizing temporal information and enriching the dynamic feature representation of the point cloud. Meanwhile, in the discriminator, we introduce the Efficient Shape module. Through dynamic graph convolution operations and stacked learning, it significantly improves the resolution efficiency of global shape information in point clouds. The final experiments show that the proposed method exhibits high practicality and superiority. The model achieves a good result on both the D-FAUST and DeformingThing4D-Animals datasets. © 2025 IEEE.

关键词： point cloud sequence Point cloud upsampling self-supervised temporal aggregation

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Metal Surface Defect Detection Algorithm Based on Improved YOLOv5

Metal Surface Defect Detection Algorithm Based on Improved Y...

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IEEE information technology and Mechatronics Engineering Conference (ITOEC)

作者： Xiaodong Su Fengchun Zhang Yurong Zhang Hongyan Xu Xu Chen College of Computer and-Information:Engineering Harbin-University of Commerce Harbin China Heilongiiang Provincial Key:Laboratory of Electronic Commerce and Information Processing Harbin China

Aiming at the problem of model misdetection and missed detection caused by the small defect and unclear features of industrial metal surfaces, this paper studies a large number of metal surface defects, and proposes a metal surface micro defect detection algorithm based on improved YOLOv5. First, this paper uses the FenceMask data enhancement method for regularization to solve the problem of few sample images. Then an enhanced multi-scale feature fusion pyramid network DS-FPN is proposed by introducing depthwise separable convolution, dilated convolution and spatial attention mechanism, so that the model can improve the ability to extract key information of images without adding additional parameters and calculations. An adaptive channel spatial attention mechanism SCBAM is also proposed, which adds non-local information to the original interaction with only local information, improving the feature expression ability of the model. Finally, through experimental verification, the detection accuracy of the model in this paper on the public data set GC10-DET has reached 74.9%, which is 4.7% higher than the original YOLOv5 model.

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