检索结果-南通市图书馆

A Novel Attention-based global and Local information Fusion Neural Network for Group Recommendation

Machine Intelligence Research 2022年第4期19卷 331-346页

作者： Song Zhang Nan Zheng Dan-Li Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Due to the popularity of group activities in social media,group recommendation becomes increasingly significant.It aims to pursue a list of preferred items for a target group.Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users.However,these methods may suffer from data sparsity problem.Except for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and items.Such interactions,which take place in the range of a group,form a local view of a certain group.In addition to local information,groups with common interests may also show similar tastes on items.Therefore,group representation can be conducted according to the similarity among groups,which forms a global view of a certain group.In this paper,we propose a novel global and local information fusion neural network(GLIF)model for group recommendation.In GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local representation.Moreover,our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups.Then,it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation.Finally,group recommendation is conducted under neural collaborative filtering(NCF)framework.Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

关键词： Group recommendation attentive neural network(ANN) global information local information recommender system

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Self-supervised recalibration network for person re-identification

引用

Defence Technology（防务技术） 2024年第1期31卷 163-178页

作者： Shaoqi Hou Zhiming Wang Zhihua Dong Ye Li Zhiguo Wang Guangqiang Yin Xinzhong Wang Shenzhen Institute of information Technology Shenzhen518172China University of Electronic Science and Technology of China Chengdu611731China Kash Institute of Electronics and information Industry Kashi844099China

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

关键词： Person re-identification Attention mechanism global information Local information Adaptive weighted fusion

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

global and local information combined to detect singular points in fingerprint images

引用

Science China(information Sciences) 2013年第6期56卷 203-215页

作者： FAN LingLing WANG ShuGuang GUO TianDe Institute of System Engineering and Control Beijing Jiaotong University Institute of Automation Chinese Academy of Sciences School of Mathematical Sciences of Graduate University Chinese Academy of Sciences

An algorithm is proposed,which combines global and local information of fingerprint images to detect singular points.It’s mathematically proven that normal lines of gradient of double orientation field(GDOF) pass through singular points.Normal lines of GDOF use rather global information to detect candidate singular points.Fingerprint image is divided into blocks and normal lines of GDOF are drawn.The number of normal lines that pass through each block is accumulated.The block that has the maximum number corresponds to a candidate singular point.It can be seen as a kind of Hough transform(HT).As candidate singular points detected by global information may have a little warp from their real positions,it’s necessary to use local information to refine their positions.Poincare index is chosen,and uses local information to refine the candidate singular points.This makes our algorithm more robust to noise than methods that only use local information.What’s more,the pairs of the detected singular points are used to classify fingerprint.Experimental results show that our algorithm performs well and fast enough for real time application in databases NIST-4.

关键词： global information local information normal lines of GDOF Poincare index singular points

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Automatic Facial Expression Recognition Using both Local and global information

Automatic Facial Expression Recognition Using both Local and...

引用

第25届中国控制会议

作者： Feng Xiaoyi,Lv Baohua ,Li Zhen School of Electronic and information, Northwestern Polytechnic University ,Xi''an 710072

＜正＞A novel approach to automatic facial expression recognition from static images is proposed in this paper. First. Active Appearance Model （AAM） is used to locate facial feature points automatically. Then,both local texture information and local shape information in these points are extracted and are combined with global texture information for face presentation. Finally,the Linear Programming （LP） technique is used for classification. Experimental results demonstrate an average recognition accuracy of 83. 6% on the JAFFE database, which shows that the proposed method is promising.

关键词： Feature detection AAM Local information global information

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Deepwalk-aware graph convolutional networks

引用

Science China(information Sciences) 2022年第5期65卷 81-95页

作者： Taisong JIN Huaqiang DAI Liujuan CAO Baochang ZHANG Feiyue HUANG Yue GAO Rongrong JI Media Analytics and Computing Lab Department of Computer Science and Technology School of InformaticsXiamen University Media Analytics and Computing Lab Department of Artificial Intelligence School of InformaticsXiamen University School of Automation Science and Electrical Engineering Beihang University Tencent Youtu Lab School of Software Tsinghua University

Graph convolutional networks(GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph data.The fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer,and Pubmed citation networks.

关键词： graph convolutional networks global information fusion node classification

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network

引用

Computer Modeling in Engineering & Sciences 2020年第4期123卷 201-216页

作者： Yao Ma Xibiao Cai Fuming Sun School of Electronics and information Engineering Liaoning University of TechnologyJinzhou121001China School of information and Communication Engineering Dalian Minzu UniversityDalian116600China

Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems.Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information.Actually,the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image.In light of this,we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network,which integrates both global information and local information of an image.We first adopt the image pyramid method to generate four scale images required for network input and then provide two network models by respectively using two fusion strategies to evaluate image quality.In order to better adapt to the quality assessment of the entire image,we use two different loss functions in the training and validation phases.The superiority of the proposed method is verified by several different experiments on the LIVE datasets and TID2008 datasets.

关键词： Image pyramid global information local information image distortion

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Modified Fuzzy C-Means for Bias Field Estimation and Segmentation of Brain MR Image 25th

A Modified Fuzzy C-Means for Bias Field Estimation and Segme...

引用

第25届中国控制与决策会议

作者： Zhang Shi She Lihuang Lu Li Zhong Hua Northeastern University

ISBN: (纸本)9781467355339

Fuzzy C-means clustering （FCM） algorithm is good at solving the ambiguities and uncertainties in the image, and the modified FCM has been widely used in solving the intensity inhomogeneity problem. Bias-corrected FCM （BCFCM） is very useful for noise and intensity inhomogeneity image segmentation, but it can’t estimate accurately the pixels on the boundary especially in the regions with heavy level of intensity inhomogeneous. In this paper, we present a novel algorithm for brain magnetic resonance imaging （MRI） Image segmentation and intensity inhomogeneity estimation based on BCFCM. The proposed algorithm introduces the global intensity information into the algorithm BCFCM, for the smooth bias field estimation and more accurate segmentations. The proposed method has been successfully applied to MR brain images, and experiment results show that this method is superior to FCM, BCFCM and some other approaches.

关键词： Bias field Fuzzy C-means clustering mage segmentation global information MR imaging

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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：