检索结果-内蒙古大学图书馆

Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

International Journal of computers and applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

引用

computers, Materials & Continua 2024年第2期78卷 2159-2179页

作者： Jinyang Yu Xiao Zhang Jinjiang Wang Yuchen Zhang Yulong Shi Linxuan Su Leijie Zeng School of Computer Science Northwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityXi’an710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi’an710072China

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy *** distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high *** address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage *** architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and *** on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw *** off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data *** provide a unified access interface for user-friendly system *** testing validates the system’s reliability and stable *** proposed approach significantly enhances storage capacity compared to standalone blockchain *** reliability tests consistently yield positive *** average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

关键词： On-chain and off-chain collaboration blockchain distributed storage system hyperledger fabric IPFS cluster

来源：评论

学校读者我要写书评

暂无评论

NOMA in Flexible Optical Access Network: Principle, Advantage and Challenges 21

NOMA in Flexible Optical Access Network: Principle, Advantag...

引用

21st International Conference on Optical Communications and Networks, ICOCN 2023

作者： Chai, Jia Feng, Nan Ma, Mingyi Li, Zhe Li, Shaobo Beijing Institute of Computer Technology and Application China The 54th Research Institute of Cetc Shijiazhuang050081 China Hebei Key Laboratory of Photonic Information Technology and Application Shijiazhuang050081 China

We summarize the current principle, advantage, and challenges of power domain non-orthogonal multiple access technology which exploited the benefits of different channel gains of passive optical network to improve the... 详细信息

ISBN: (纸本)9798350343502

关键词： Passive optical networks

来源：评论

学校读者我要写书评

暂无评论

Music conditioned 2D hand gesture dance generation with HGS

Music conditioned 2D hand gesture dance generation with HGS

引用

作者： Zhou, Dian Liu, Shiguang Xu, Qing School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China

In recent years, the short video industry is booming. However, there are still many difficulties in the action generation of virtual characters. We observed that on the short video social platform, "hand gesture dance" is a very popular short video form. However, its development is limited by the professionalism of choreography. In order to solve these problems, we propose an intelligent choreography framework, which can generate new gesture sequences for unseen audio based on pairing data in the database. Our framework adopts multimodal method and obtains excellent results. In additional, we collected and produced the first and largest pair labeled hand gesture dance data set. Various experiments showed that our results not only generate smooth and rich action sequences, but also collect some semantic information contained in the audio. © 2023 John Wiley & Sons Ltd.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Visible and Infrared Image Fusion for Object Detection: A Survey

Visible and Infrared Image Fusion for Object Detection: A Su...

引用

International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Sun, Yuxuan Meng, Yuanqin Wang, Qingbo Tang, Minghua Shen, Tao Wang, Qingwang School of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Naval Research Academy Beijing China

ISBN: (纸本)9789819708543

Thanks to the advancements of Deep Learning (DL) Algorithms. DL-based object detection models has witnessed remarkable success in the past few years. By leveraging deep convolutional neural networks (CNNs) and other deep learning models, rich feature representations can be effectively extracted from visible (RGB) images for object detection. However, for challenging scenarios such as low-light conditions and haze, the performance of object detection using visible images is often not satisfactory. On the other hand, thermal camera, which is unaffected by lighting conditions, can penetrate through low-light and hazy environments to capture object images. However, infrared images lack edge and texture information of objects. Recognizing the complementary nature of visible and infrared images, researchers have explored the fusion of visible and infrared images for object detection, yielding promising research outcomes. This paper provides an analysis of the current research status of visible and infrared image fusion for object detection. Firstly, the fusion models are categorized into three kinds: pixel-level fusion, feature-level fusion, and decision-level fusion. Several models within each category are discussed. Furthermore, this paper summarizes five datasets that can be utilized for training RGB-Infrared object detection models and compares the experimental results of selected models on the KAIST dataset. Lastly, the paper concludes with a summary of existing challenges in the field and offers some reflections on future directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing 24

Bit-mask Robust Contrastive Knowledge Distillation for Unsup...

引用

33rd ACM Web Conference, WWW 2024

作者： He, Liyang Huang, Zhenya Liu, Jiayu Chen, Enhong Wang, Fei Sha, Jing Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://***/hly1998/BRCD. © 2024 Owner/Author.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

RGB-guided hyperspectral image super-resolution with deep progressive learning

引用

CAAI Transactions on Intelligence technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Representing Multiview Time-Series Graph Structures for Multivariate Long-Term Time-Series Forecasting

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2651-2662页

作者： Wang, Zehao Fan, Jin Wu, Huifeng Sun, Danfeng Wu, Jia Hangzhou Dianzi University Department of Computer Science and Technology Hangzhou310018 China Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries Hangzhou310018 China Zhejiang Normal University Laboratory of Intelligent Education Technology and Application of Zhejiang Province Hangzhou310018 China Macquarie University School of Computing SydneyNSW2113 Australia

Multivariate long-term time-series forecasting tasks are very challenging tasks in many real-world application areas. Recently, researchers focus on designing robust and effective methods, and have made considerable progress. However, there are several issues with existing models that need to be overcome. First, the lack of a relationship structure between multivariate variables needs to be addressed. Second, most models only have a weak ability to capture local dynamic changes across the entire long-term time-series. Third, current models suffer from high computational complexity and unsatisfactory accuracy. To figure out the abovementioned issues, we propose an effective and efficient method called multiview time-series graph structure representation (MTGSR). MTGSR uses GCNs to construct topological relationships in the multivariate time-series from three different perspectives: time, dimension, and crossing segments. Variation trends in different dimensions are extracted through a difference operation to construct topological maps that reflect the correlations between different dimensions. To capture the dynamically changing characteristics of fluctuation correlations between adjacent local sequences, MTGSR constructs a cross graph by calculating correlation coefficients between adjacent local sequences. Extensive experiments show that MTGSR reduces errors by 17.41% over the state of the art. In addition, memory use is decreased by 66.52% and the running time is reduced by 78.09%. © 2020 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Improving Global Precipitation in Numerical Weather Prediction Systems based on Deep Learning Techniques 25

Improving Global Precipitation in Numerical Weather Predicti...

引用

25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhao, Haixiang Zhang, Guojing Du, Ming Wang, Xiaoying Qinghai University Department of Computer Technology and Applications Xining810016 China Geospatial Information Technology and Application Qinghai Provincial Key Laboratory Xining810016 China

ISBN: (纸本)9798350330014

Accurate weather forecasts are crucial for many aspects of people's lives. Traditional short-term precipitation forecasting methods mainly rely on numerical weather predictions based on meteorological dynamics modelling. However, numerical weather predictions based on high-resolution physical models require hours of computation on supercomputers. Deep learning networks possess strong feature extraction capabilities, directly mapping images, meteorological data, and other information to high-dimensional feature spaces. Neurons extract abstract features, enabling continuous training and learning for predictions. Therefore, deep learning provides a new solution for short-term precipitation forecasting. In the past, deep learning has been used for precipitation forecasting by employing radar echo extension methods;however, these methods are limited to short-term forecasts and do not utilize other meteorological data, resulting in inaccurate predictions. This study proposes a model containing an attention mechanism, a convolutional extended short-term neural network (Att-PredRNN), based on 5.625° (32×64 grid points) resolution ECMWF reanalysis data and three years of global meteorological and daily precipitation data. The network uses multiple meteorological fields as input factors to predict spatial precipitation distribution for 24-hour precipitation forecasting. The model significantly improves the capture of spatial and temporal correlations through the attention matrices focusing on different parts of the input sequence. Experimental results show that the proposed Att-PredRNN model outperforms other comparison models in terms of RMSE, CSI, POD, and FAR overall and dramatically enhances the accuracy of short-term precipitation forecasts. Our code and data are publicly available. © 2023 IEEE.

关键词： Supercomputers

来源：评论

学校读者我要写书评

暂无评论

Vision based intelligent traffic light management system using Faster R‐CNN

引用

CAAI Transactions on Intelligence technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：