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Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 2579-2588页

作者： Li, Jiaxin Wang, Hongxing Tan, Jiawei Ou, Zhilong Yuan, Junsong Chongqing University School of Big Data and Software Engineering Chongqing401331 China State University of New York Department of Computer Science and Engineering BuffaloNY14260 United States

Understanding 3D object shapes necessitates shape representation by object parts abstracted from results of instance and semantic segmentation. Promising shape representations enable computers to interpret a shape with meaningful parts and identify their repeatability. However, supervised shape representations depend on costly annotation efforts, while current unsupervised methods work under strong semantic priors and involve multi-stage training, thereby limiting their generalization and deployment in shape reasoning and understanding. Driven by the tendency of high-dimensional semantically similar features to lie in or near low-dimensional subspaces, we introduce a one-stage, fully unsupervised framework towards semantic-aware shape representation. This framework produces joint instance segmentation, semantic segmentation, and shape abstraction through sparse representation and feature alignment of object parts in a high-dimensional space. For sparse representation, we devise a sparse latent membership pursuit method that models each object part feature as a sparse convex combination of point features at either the semantic or instance level, promoting part features in the same subspace to exhibit similar semantics. For feature alignment, we customize an attention-based strategy in the feature space to align instance- and semantic-level object part features and reconstruct the input shape using both of them, ensuring geometric reusability and semantic consistency of object parts. To firm up semantic disambiguation, we construct cascade unfrozen learning on geometric parameters of object parts. Experiments conducted on benchmark datasets confirm that our approach results in instance- and semantic-level joint segmentation and shape abstraction with repeatable primitives, providing coherent semantic interpretations of 3D object shapes across categories in a one-stage, fully unsupervised manner, without relying on annotations or heuristic semantic priors. Code will be

关键词： Semantic Segmentation

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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Computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks

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science China(Information sciences) 2025年第4期68卷 363-379页

作者： Xuehui WANG Qingbo LI Wen ZHU Feng SHU Mengxing HUANG Fuhui ZHOU Riqing CHEN Cunhua PAN Yongpeng WU Jiangzhou WANG School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University School of Information and Communication Engineering Hainan University Collaborative Innovation Center of Information Technology Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University National Mobile Communications Research Laboratory Southeast University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent

Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface(IRS) has garnered significant attention. In this paper, an amplify-and-forward(AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio(SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance(HP) method and a low-complexity(LC) method. The HP method is based on the semidefinite relaxation and fractional programming(SDR-FP)algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter,generalized power iteration, and generalized Rayleigh-Ritz(WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0%rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.

关键词： double-fading intelligent reflecting surface active elements passive elements hybrid IRS AF relay

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Readout electronics for the gamma detector of the HIRFL-CSR external target facility

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Nuclear science and Techniques 2025年第2期36卷 71-81页

作者： Xian-Qin Li Hai-Bo Yang Xiao-Meng Ma Chao-Jie Zou Tao Liu Xian-Cai Zhou Duo Yan Yang-Zhou Su Shu-Wen Tang Shi-Tao Wang Yu-Hong Yu Zhi-Yu Sun Cheng-Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China School of Physics and Electronic Engineering Northwest Normal UniversityLanzhou 730070China School of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China

The Cooling Storage Ring of the Heavy Ion Research Facility in Lanzhou(HIRFL-CSR)was constructed to study nuclear physics,atomic physics,interdisciplinary science,and related *** External Target Facility(ETF)is located in the main ring of the *** gamma detector of the ETF is built to measure emitted gamma rays with energies below 5 MeV in the center-of-mass frame and is planned to measure light fragments with energies up to 300 *** readout electronics for the gamma detector were designed and *** readout electronics consist of thirty-two front-end cards,thirty-two readout control units(RCUs),one common readout unit,one synchronization&clock unit,and one sub-trigger *** using the real-time peak-detection algorithm implemented in the RCU,the data volume can be significantly *** addition,trigger logic selection algorithms are implemented to improve the selection of useful events and reduce the data *** test results show that the integral nonlinearity of the readout electronics is less than 1%,and the energy resolution for measuring the 60 Co source is better than 5.5%.This study discusses the design and performance of the readout electronics.

关键词： HIRFL-CSR Gamma detector External target facility Readout electronics Readout control unit Common readout unit Peak-detection algorithm

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Skeleton-Based Action Recognition Using Graph Convolutional Network with Pose Correction and Channel Topology Refinement

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Computers, Materials & Continua 2025年第4期83卷 701-718页

作者： Yuxin Gao Xiaodong Duan Qiguo Dai School of Engineering Guangzhou College of Technology and BusinessFoshan528138China School of Computer Science and Engineering Dalian Minzu UniversityDalian116000China SEAC key Laboratory of Big Data Applied Technology Dalian Minzu UniversityDalian116000China

Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous ***,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion *** video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation *** this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action ***,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature ***,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature ***,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition *** evaluate the model using top-1 and top-5 classification *** the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,*** the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.

关键词： Pose correction multi-stream fusion GCN action recognition

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A HIGH ORDER SCHEME FOR FRACTIONAL DIFFERENTIAL EQUATIONS WITH THE CAPUTO-HADAMARD DERIVATIVE

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Journal of Computational Mathematics 2025年第3期43卷 615-640页

作者： Xingyang Ye Junying Cao Chuanju Xu School of Science Jimei UniversityXiamen 361021China School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen UniversityXiamen 361005China

In this paper,we consider numerical solutions of the fractional diffusion equation with theαorder time fractional derivative defined in the Caputo-Hadamard sense.A high order time-stepping scheme is constructed,analyzed,and numerically *** contribution of the paper is twofold:1)regularity of the solution to the underlying equation is investigated,2)a rigorous stability and convergence analysis for the proposed scheme is performed,which shows that the proposed scheme is 3+αorder *** numerical examples are provided to verify the theoretical statement.

关键词： Caputo-Hadamard derivative Fractional differential equations High order scheme Stability and convergence analysis

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Enhancing Situational Awareness: Anomaly Detection using Real-Time Video Across Multiple Domains

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IEEE Access 2025年 13卷 73680-73696页

作者： Singh, Rajan Pal, Amrit Mishra, Shruti Chowdhury, Abishi Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai600127 India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai600127 India

Though it is challenging, anomaly detection in real-time video data has significant implications in many disciplines like public safety, education, and agriculture. This paper proposes a novel approach to handle the complexity of dynamic real-world anomaly detection scenarios using three state of the art machine learning models: Convolutional Neural Networks (CNN), Region-based Convolutional Neural Network (R-CNN), and You Only Look Once (YOLO). The proposed system is precisely crafted to fast inform authorities of irregularities, so improving situational awareness and security procedures. Using trained models, applying the most effective one for real-time detection, and extensively investigating video data are part of the study method suggested here. The key objectives are to guarantee flexibility to match numerous circumstances and reduce false positives. First findings show the system’s good performance in numerous settings, thereby offering the basis for additional development and study. Especially, following rigorous training and testing, every model displayed varying degrees of accuracy. Emphasizing its effectiveness in focused detection tasks, the CNN model shown a remarkable accuracy of 73% in spotting anomalies inside specific areas of interest. R-CNN, on the other hand, shown greater performance in demanding environments and an accuracy of 80%. Especially appropriate for quick anomaly identification, YOLO, renowned for its speed and accuracy in real-time object detection, shone out with an amazing 91%. This work greatly helps the field of surveillance technologies by providing specific techniques to increase security and safety in useful surroundings. Emphasizing the transformational possibilities of novel anomaly detection systems, this work underlines the importance of constant research and deployment of innovative concepts to boost security and safety measures. © 2013 IEEE.

关键词： Convolutional neural networks

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SAMFNet: Scene-aware sampling and multi-stage fusion for multimodal 3D object detection

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Alexandria engineering Journal 2025年 126卷 90-104页

作者： Wang, Baotong Xia, Chenxing Gao, Xiuju Ge, Bin Li, Kuan-Ching Fang, Xianjin Zhang, Yan Yang, Yuan College of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China College of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China Department of Computer Science and Information Engineering Providence University Taichung43301 China The School of Electronics and Information Engineering Anhui University Hefei230039 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

Recently, multimodal 3D object detection (M3OD) that fuses the complementary information from LiDAR data and RGB images has gained significant attention. However, the inherent structural differences between point clouds and images pose fusion challenges, significantly hindering the exploration of correlations within multimodal data. To address this issue, this paper introduces an enhanced multimodal 3D object detection framework (SAMFNet), which leverages virtual point clouds generated from depth completion. Specifically, we design a scene-aware sampling module (SASM) that employs tailored sampling strategies for different bins based on the density distribution of point clouds. This effectively alleviates the detection bias problem while ensuring the key information of virtual points, significantly reducing the computational cost. In addition, we introduce a multi-stage feature fusion module (MSFFM) that embeds point-level and regional-adaptive feature fusion strategies to generate more informative multimodal features by fusing features with different granularities. To further improve the accuracy of model detection, we also introduce a confidence prediction branch unit (CPBU), which improves the detection accuracy by predicting the confidence of feature classification in the intermediate stage. Extensive experiments on the challenging KITTI dataset demonstrate the validity of our model. © 2025 The Authors

关键词： Image fusion

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Neighbor-relation aware low-rank multi-view subspace clustering

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Multimedia Systems 2025年第3期31卷 1-16页

作者： Jia, Hongjie Wang, Tengteng Song, Heping School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang212013 China

Low-rank multi-view subspace clustering seeks to uncover complex relationships in high-dimensional data by leveraging multiple views and low-rank representations. However, two key challenges persist: (1) Existing methods emphasize low-rank consistency through nuclear norm regularization but often overlook the unique low-rank features of individual views. (2) Constructing the affinity matrix directly from the coefficient matrix can introduce excessive connections, degrading clustering performance. To address these challenges, we propose neighbor-relation aware low-rank multi-view subspace clustering (NLMSC), a unified model that integrates view-specific low-rank structure learning with neighbor-aware affinity fusion. NLMSC decomposes the coefficient matrix into smaller factor matrices to capture the underlying low-rank structures of each view and constructs a sparser, more interpretable affinity matrix by reliable neighbor selection. These affinity matrices are then fused into a block-diagonal consensus graph through a self-weighting strategy for spectral clustering. Extensive experiments on diverse multi-view datasets demonstrate NLMSC’s superior performance and effectiveness in challenging clustering tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Low-rank structure Multi-view clustering Neighbor-relation awareness Subspace clustering

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ML-Based College Predictor Telegram Bot Indian engineering Colleges 1

ML-Based College Predictor Telegram Bot Indian Engineering C...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： More, Amruta Patil, Satchal Savale, Aditya Satle, Prafull Tilwane, Krishna Shinde, Sanket Ajeenkya DY Patil School of Engineering Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350355611

This Research aimed at the challenge of finding college for students, but this is complex task as students are unable to find best institutes based on entrance exam scores in India. To bounce on that, we focus on the development of a College Predictor Bot, which uses some key factors notably JEE and CET scores and predicts admission to different Indian colleges. You are trained on years of admissions data from hundreds of colleges, with students of all shapes and sizes and performance levels. In terms of methodology used in the study, a combination of random forest and decision tree models were used for analysing the exam scores and make findings. The ML-model is subject to a lot of statistics and testing, and the analysis results in highly valuable co-relations between performance on entrance exams and admitting results. Our results show that College Predictor Bot accurately predicts admissions, therefore helping students know what colleges are suitable. The larger implications [of this tool] are that this might make the college selection process easier and provide a slightly more transparent and informed way of getting deserved seat in the Eductaion system of Bharat. © 2025 IEEE.

关键词： Students

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