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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jinxia Xie Bineng Zhong Zhiyi Mo Shengping Zhang Liangtao Shi Shuxiang Song Rongrong Ji Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University School of Computer Science and Technology Harbin Institute of Technology Media Analytics and Computing Lab School of Informatics Xiamen University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However, most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently, the spatio-temporal information is far away from being fully explored. To alleviate this issue, we propose an adaptive tracker with spatio-temporal transformers (named AQA-Track), which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components. Firstly, we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion. Then, we design a novel attention mechanism for the interaction of existing queries to generate a new query in current frame. Finally, based on the initial target template and learnt autoregressive queries, a spatio-temporal information fusion module (STM) is designed for spatiotemporal formation aggregation to locate a target object. Benefiting from the STM, we can effectively combine the static appearance and instantaneous changes to guide robust tracking. Extensive experiments show that our method significantly improves the tracker's performance on six popular tracking benchmarks: LaSOT, LaSOT ext , TrackingNet, GOT-10k, TNL2K, and UAV123. Code and models will be https://***/orgs/GXNU-Zhonglab.

关键词： Adaptation models Visualization computer vision Target tracking Computational modeling Transformers Market research

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Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China

Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-label generation based unsupervised domain adaptation paradigm, which discriminates the predicted keypoints of unlabeled data based on the skeleton position consistency, has demonstrated effectiveness for such problems. However, existing methods generate pseudo-labels with massive false positives, because they cannot effectively distinguish sample pairs with the same errors. In this study, we propose a cross-domain animal pose estimation model from a novel perspective of skeleton anomaly learning. We construct a graph contrastive learning mechanism to acquire the skeleton anomaly-aware knowledge, which enables the generation of accurate pseudo-labels for target domain and imposes graph constraint on unlabeled data. And a skeleton anomaly-feedback based domain adaptation framework is designed to facilitate implicit alignment of object-specific features and joint training of cross-domain. Besides, we propose a novel rat pose dataset named UDARP-9.4K to address the gap of small-sized animal pose datasets encompassing diverse experimental scenarios. The related datasets are reviewed and evaluated in detail. Extensive experiments are conducted on UDARP-9.4K and two public datasets to demonstrate the superiority of the proposed model in cross-scenarios and cross-species animal pose estimation tasks. Further analysis reveals the effectiveness of the proposed model for skeleton structure feature learning. The UDARP-9.4K dataset is available here. © 1991-2012 IEEE.

关键词： Invertebrates

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Domain Disentangled Generative Adversarial network for Zero-Shot Sketch-Based 3D Shape Retrieval

arXiv

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arXiv 2022年

作者： Xu, Rui Han, Zongyan Hui, Le Qian, Jianjun Xie, Jin PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Sketch-based 3D shape retrieval is a challenging task due to the large domain discrepancy between sketches and 3D shapes. Since existing methods are trained and evaluated on the same categories, they cannot effectively recognize the categories that have not been used during training. In this paper, we propose a novel domain disentangled generative adversarial network (DD-GAN) for zero-shot sketch-based 3D retrieval, which can retrieve the unseen categories that are not accessed during training. Specifically, we first generate domain-invariant features and domain-specific features by disentangling the learned features of sketches and 3D shapes, where the domain-invariant features are used to align with the corresponding word embeddings. Then, we develop a generative adversarial network that combines the domain-specific features of the seen categories with the aligned domain-invariant features to synthesize samples, where the synthesized samples of the unseen categories are generated by using the corresponding word embeddings. Finally, we use the synthesized samples of the unseen categories combined with the real samples of the seen categories to train the network for retrieval, so that the unseen categories can be recognized. In order to reduce the domain shift problem, we utilized unlabeled unseen samples to enhance the discrimination ability of the discriminator. With the discriminator distinguishing the generated samples from the unlabeled unseen samples, the generator can generate more realistic unseen samples. Extensive experiments on the SHREC'13 and SHREC'14 datasets show that our method significantly improves the retrieval performance of the unseen categories. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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KGAPG: Knowledge-Aware Neural Group Representation Learning for Attentive Prescription Generation of Traditional Chinese Medicine

KGAPG: Knowledge-Aware Neural Group Representation Learning ...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuchen Li Wei Wang Jieyue He Key Lab of Computer Network & Information Integration MOE Southeast University Nanjing China

ISBN: (纸本)9781665429825

Prescriptions play an essential role in the process of Traditional Chinese Medicine (TCM) diagnosis and treatment. Prescription generation is to generate a set of herbs to treat the symptoms of a patient by analyzing the relationship between symptoms and herbs. Although there have been a couple of studies to generate prescriptions, they have ignored the implicit relationship between the different symptoms of the patients. In addition, abundant semantic information and interpretability of the knowledge graph can help to portray the complicated relationships between the various modules of TCM. Therefore, this paper proposes a Knowledge-Aware neural Group representation learning model for Attentive Prescription Generation of Traditional Chinese Medicine (KGAPG), which regards the prescription generation task as a group recommendation problem. More specifically, multiple symptoms of a patient are considered as a symptom group and the complicated semantic information between symptoms and herbs can be captured by the knowledge graph. The syndrome information of multiple symptoms which is summarized by a group aggregation method based on the attention mechanism is applied to simulate the actual process of TCM diagnosis and treatment. The experiment results demonstrate that KGAPG is effective on a TCM prescription benchmark dataset, and its evaluation indicators of Precision, Recall and NDCG exceed other state-of-the-art methods.

关键词： Representation learning Conferences Semantics Benchmark testing Task analysis Bioinformatics

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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment:Advancing Big Data Intelligence in Control Systems

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computers, Materials & Continua 2025年第6期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu ChongLv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and TechnologyChina University of Petroleum(East China)Qingdao266580China Shandong Key Laboratory of Intelligent Oil&Gas Industrial Software Qingdao266580China Library of Shanghai Lixin University of Accounting and Finance Shanghai201209China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer ScienceJinan250014China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of ChinaBeijing100081China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPUTaicang215400China Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University) Ministry of EducationKunming650092China

As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational *** this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)*** main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network *** integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC ***,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading *** results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of ***,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing ***,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other *** findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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Relay-Enabled Backscatter Communications: Linear Mapping and Resource Allocation

arXiv

引用

arXiv 2023年

作者： Xu, Rui Shi, Liqin Ye, Yinghui Sun, Haijian Zheng, Gan The Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications China The School of Electrical and Computer Engineering The University of Georgia AthensGA United States The School of Engineering University of Warwick CoventryCV4 7AL United Kingdom

Relay-enabled backscatter communication (BC) is an intriguing paradigm to alleviate energy shortage and improve throughput of Internet-of-Things (IoT) devices. Most of the existing works focus on the resource allocation that considered the unequal and continuous time allocation for both source-relay and relay-destination links. However, the continuous time allocation may be infeasible since in practice, the time allocation shall be carried out in integral multiple of the subframe duration unit. In this article, we study a discrete time scheme from the perspective of frame structure, where one transmission block is divided into two phases and the linear mapping is employed as a re-encoding method to determine the number of subframes for both phases and the power allocation for each subframe in a relay-enabled BC system. Based on this, we derive an accurate system-throughput expression and formulate a mixed-integral non-convex optimization problem to maximize the system throughput by jointly optimizing the power reflection coefficient (PRC) of the IoT node, the power allocation of the hybrid access point (HAP) and the linear mapping matrix, and solve it via a three-step approach. First, we derive the optimal eigenvalues of the linear mapping matrix via introducing auxiliary variables and exploiting Karush-Kuhn-Tucker (KKT) conditions. Second, we employ the time-sharing technology to convert the discrete optimization problem to a continuous nonconvex problem and solve it by leveraging successive convex approximation (SCA) and monotonicity or bisection method. Finally, an integer conversion strategy is designed to find a suboptimal number of subframes for both phases, based on which the continuous variables are further optimized. According to the above approaches, we propose a low complexity iterative algorithm to obtain the throughput maximization-based resource allocation solution. Numerical results analyze the performance of our proposed algorithm, verify the superio

关键词： Internet of things

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An RTDNN-Based Hybrid Precoding and Combining Joint Optimization Algorithm for MUPC-MIMO Systems

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and networking 2025年

作者： Liu, Fulai Liu, Yubiao Yang, Xianghuan Duan, Zhuoyao Shi, Baozhu Ji, Li Du, Ruiyan Northeastern University at Qinhuangdao Lab of Key Technology of Millimeter-wave Large-scale MIMO System Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Hebei University College of Mathematics and Information Science Baoding071002 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Qinhuangdao Vocational and Technical College Qinhuangdao066100 China School of Computer Science and Engineering Northeastern University Shenyang110004 China

To achieve a satisfactory balance between energy efficiency (EE) and spectral efficiency (SE) for hybrid precoding and combining (HPC), this paper presents a two-stage joint optimization HPC algorithm based on residual deep neural network (DNN) for multi-user partially connected (MUPC) structure, namely RTDNN algorithm. In the proposed algorithm, a two-stage framework is designed to optimize analog and digital coders at the transmitter and receiver simultaneously, which can effectively preserve coupling relationship of coders between the transmitter and receiver. Specially, the RTDNN architecture is mainly divided into two stage networks, which includes analog stage and digital stage networks. The analog stage network is developed to obtain the optimal analog precoder and combiner simultaneously. On this basis, the digital stage network is constructed to solve the digital precoder and combiner concurrently. To further improve the prediction accuracy of RTDNN-based HPC, residual modules are adopted into the developed two stage networks. Moreover, the proposed RTDNN is trained with unsupervised learning. Simulation results show that the EE of the proposed algorithm is at least enhanced by 0.6 bits/Hz/J with satisfactory SE than other methods. © 2017 IEEE.

关键词： Deep neural networks

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Secrecy Performance Analysis of RIS-Aided Hybrid RF/FSO networks

Secrecy Performance Analysis of RIS-Aided Hybrid RF/FSO Netw...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Menghan Wu Dawei Wang Lingtong Min Yixin He Chen Gao Li Zhen Keping Yu School of Electronics and Information Northwestern Polytechnical University Xi'an China College of Information Science and Engineering Jiaxing University Jiaxing China Jiaxing Key Laboratory of Smart Transportations Jiaxing China Department of Electrical and Computer Engineering National University of Singapore Singapore Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China Graduate School of Science and Engineering Hosei University Tokyo Japan

The proposed study introduces a reconfigurable intelligent surface (RIS)-aided hybrid radio frequency (RF)/free space optical (FSO) system with an unmanned aerial vehicle (UAV) relay to enable an ultra-dense sixth-generation (6G) network. The channels for RF and FSO are represented by Rayleigh and Gamma-Gamma probability distributions, correspondingly. Additionally, the network includes a terrestrial eavesdropper that follows the Nakagami-m distribution, attempting to breach confidential information. To counter this threat, RIS technology is used to enhance the hybrid system's secrecy. The study conducts a closed-form analysis of the secrecy outage probability (SOP) and obtains its asymptotic expression for determining the diversity order and coding gain. Theoretical findings have been confirmed through thorough numerical simulations implemented with the Monte-Carlo approach. The findings demonstrate the RIS technology's effectiveness in enhancing the network's secrecy performance.

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Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching

arXiv

引用

arXiv 2022年

作者： Bian, Yikai Hui, Le Qian, Jianjun Xie, Jin PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data. Most of existing methods use global-level feature alignment to transfer the knowledge from the source domain to the target domain, which may cause the semantic ambiguity of the feature space. In this paper, we propose a graph-based framework to explore the local-level feature alignment between the two domains, which can reserve semantic discrimination during adaptation. Specifically, in order to extract local-level features, we first dynamically construct local feature graphs on both domains and build a memory bank with the graphs from the source domain. In particular, we use optimal transport to generate the graph matching pairs. Then, based on the assignment matrix, we can align the feature distributions between the two domains with the graph-based local feature loss. Furthermore, we consider the correlation between the features of different categories and formulate a category-guided contrastive loss to guide the segmentation model to learn discriminative features on the target domain. Extensive experiments on different synthetic-to-real and real-to-real domain adaptation scenarios demonstrate that our method can achieve state-of-the-art performance. Our code is available at https://***/BianYikai/ PointUDA. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

arXiv

引用

arXiv 2022年

作者： Shen, Yaqi Hui, Le Jiang, Haobo Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation. Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss, and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance. Our code is available at https://***/supersyq/RIENet. Copyright © 2022, The Authors. All rights

关键词： Surface measurement

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