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Graph convolutional network combined with random walks and graph attention network for node classification

The Journal of China Universities of Posts and Telecommunications 2024年第3期31卷 1-14页

作者： Chen Yong Xie Xiaozhu Weng Wei College of Computer and Information Engineering Xiamen University of TechnologyXiamen 361024China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of TechnologyXiamen 361024China

Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor ***,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant *** this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise ***,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting *** model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message *** show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.

关键词： graph neural network(GNN) graph convolutional network(GCN) semi-supervised classification graph analysis

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Adaptive Illumination Normalization Approach Based on Denoising Technique for Face Recognition

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Journal of Shanghai Jiaotong university(Science) 2017年第1期22卷 45-49页

作者：练智超宋杰李杨 School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology

A novel adaptive illumination normalization approach is proposed to eliminate the effects caused by illumination variations for face recognition. The proposed method divides an image into blocks and performs discrete cosine transform(DCT) in blocks independently in the logarithm domain. For each block-DCT coefficient except the direct current(DC) component, we take the illumination as main signal and take the reflectance as "noise". A data-driven and adaptive soft-thresholding denoising technique is employed in each block-DCT coefficient except the DC component. Illumination is estimated by applying the inverse DCT in the block-DCT coefficients, and the indirectly obtained reflectance can be used in further recognition task. Experimental results show that the proposed approach outperforms other existing methods. Moreover, the proposed method does not need any prior information, and none of the parameters can be determined by experience.

关键词： face recognition illumination variation soft-thresholding denoising TP 391.4 A

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Long-range correlation analysis of urban traffic data

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Chinese Physics B 2010年第8期19卷 55-64页

作者：盛鹏王俊峰唐铁桥赵树龙 School of Computer Science Sichuan University Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University School of Transportation Science and Engineering Beijing University of Aeronautics and Astronautics

This paper investigates urban traffic data by analysing the long-range correlation with detrended fluctuation analysis. Through a large number of real data collected by the travel time detection system in Beijing, the variation of flow in different time periods and intersections is studied. According to the long-range correlation in different time scales, it mainly discuss the effect of intersection location in road net, people activity customs and special traffic controls on urban traffic flow. As demonstrated by obtained results, the urban traffic flow represents three-phase characters similar to highway traffic. Moreover, compared by the two groups of data obtained before and after the special traffic restrictions （vehicles with special numbered plates only run in a special workday） enforcement, it indicates that the rules not only reduce the flow but also avoid irregular fluctuation.

关键词： urban traffic data long-range correlation detrended fluctuation analysis special traffic restriction

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Clustering by local label approximation with extreme learning machine

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第18期6卷 508-515页

作者： Luo, Xiao-Zhuo Liu, Fang Wang, Xiao-Dong Zhou, Zhi-Guo School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an 710071 China

Clustering has been an active research area over the last decades, and has become one of most important subjects in pattern recognition and data mining. In recent years, Graph Based Clustering (GBC) has received more attentions. In GBC, as Local Learning based Clustering Algorithm (LLCA) can obtain better cluster performance, it is used widely. However, there are so many parameters in current LLCA method that we have to spend much time on selecting them. To solve this problem, a novel clustering method, named as Clustering by Local Label Approximation with Extreme Learning Machine (LLAELM) is proposed in this paper. As Extreme Learning Machine (ELM) have the advantages of fewer parameters and higher accuracy, it is applied to improve the clustering algorithm based on local learning in the proposed method. The experiments on artificial data sets and UCI data sets demonstrate that LLAELM can obtain better performance than competitive methods.

关键词： Clustering algorithms

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Multi-task learning with deep dual-path network for facial attribute recognition 9

Multi-task learning with deep dual-path network for facial a...

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Lai, Xinyu Chen, Si Wang, Da-Han Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450387835

Facial attribute recognition is a popular and challenging research topic in computer vision. In the traditional deep learning based attribute recognition methods, the mid-level network features and the differences between attribute groups are not fully explored. To solve the above problem, a deep dual-path network is proposed for facial attribute recognition. In the multi-task learning framework, two sub-networks are employed to respectively extract the features of two attribute groups, i.e., local attributes and global ones, and designed with both different scale images and different depth networks. Furthermore, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the class imbalance problem for facial attribute recognition. Experimental results on the challenging CelebA dataset show that the proposed method achieves the better performance than state-of-the-art methods. © 2020 ACM.

关键词： Deep learning

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A Survey of Person Re-identification Based on Deep Learning 10

A Survey of Person Re-identification Based on Deep Learning

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10th International Conference on Computing and Pattern Recognition, ICCPR 2021

作者： Tian, Zimin Chen, Si Wang, Da-Han Lu, Junwen School of Computer and Information Engineering Xiamen University of Technology China Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450390439

Person re-identification (Re-ID) has been a popular research topic in computer vision in recent years, and it has important application value in numerous fields, such as intelligent security. The person Re-ID task is to identify whether the pedestrians appearing under different cameras are the same person. The traditional person Re-ID methods mainly rely on the characteristics of manual design, and it has difficulty in solving the problems of person occlusion, posture change, and illumination variation. With the wide application of deep learning, the person Re-ID based on deep learning has brought new ideas for solving these problems, and has been widely concerned by scholars. This paper summarizes and analyzes the latest research trends of person Re-ID based on deep learning. In our work, the recent research works of person Re-ID are coarsely categorized into the supervised learning methods and the unsupervised learning methods according to whether the pedestrian images in the training set have real labels. We then describe the representative datasets used in the person Re-ID task. Finally, we conclude and discuss the future directions of the person Re-ID based on deep learning. © 2021 ACM.

关键词： Unsupervised learning

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Wavelength dependent loss of splice of single-mode fibers

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Journal of Central South University 2013年第7期20卷 1832-1837页

作者： YANG Bo DUAN Ji-an XIE Zheng XIAO Hong-feng State Key Laboratory of High Performance Complex Manufactory (Central South University) Changsha 410083 China Institution of Image Recognition and Computer Vision Hunan Normal University Changsha 410081 China

After reviewing three different definitions of mode field diameter of single-mode fibers, coupled efficiency calculation methods associated with lateral offset, longitude separation and wavelength, the effects produced by them, and the influences of splicing defects were discussed in detail. The regularities of the effects were studied according to the first order derivation of couple efficiency formula, and a simplified formula for couple efficiency calculation was presented under the circumstance of slight misalignment, with respect to wavelength, 2, and in a good agreement with the theoretical model. The simplified formula provides a new but simple approach to evaluate wavelength dependent couple efficiency of single-mode fibers. Theoretical analyses and numerical calculations show that, when those defects exist, the wavelength produces additional effects on the couple loss that growth of wavelength causes an increase on the couple efficiency for the lateral offset or longitude separation whereas lessens the couple efficiency due to angular misalignment or mode fields mismatching, and that the wavelength degrades the couple efficiency distinctly when λ≥2.5 μm whereas it distorts the couple slightly in range of λ≤2λ≤2 μm.

关键词： wavelength dependent loss couple efficiency single-mode fiber cut-offwavelength

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Twofold Siamese Network with Attentional Feature Fusion for Object Tracking 22

Twofold Siamese Network with Attentional Feature Fusion for ...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Huang, Huang Chen, Si Wang, Da-Han Xu, Huarong School of Computer and Information Engineering Xiamen University of Technology China Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450396110

Object tracking is still a critical and challenging problem in computer vision. More and more researchers pay attention to applying deep learning to obtain the powerful feature for robust tracking. Nowadays, feature fusion is an essential part of Siamese tracking architectures. However, the existing feature fusion methods usually provide a fixed linear aggregation of feature maps, and this combination may not be appropriate for a specific object. In this paper, a twofold Siamese network, named SD-Siam, is proposed to extract the features of the object effectively. The template branch and the search branch are both composed of a deep layer sub-network and a shallow layer sub-network, which is used for feature fusion of the different network layers. Moreover, an attentional feature fusion scheme is employed to better fuse scale-inconsistent features, where a multi-scale channel attention module is used to fuse different scales of features. In addition, we respectively evaluate similarity measures for the features of deep layer sub-networks and the fused features of the template branch and the search branch, and then these two similarity response maps are added to obtain the tracking result. Experiments show the proposed SD-Siam outperforms representative trackers on several challenging benchmarks. © 2022 ACM.

关键词： Network layers

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MoAFormer: Aggregating Adjacent Window Features into Local vision Transformer Using Overlapped Attention Mechanism for Volumetric Medical Segmentation 11

MoAFormer: Aggregating Adjacent Window Features into Local V...

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11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Luo, Yixi Yin, Huayi Du, Xia Department of Computer and Information Engineering Fujian Provincial Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

The window-based attention is used to alleviate the problem of abrupt increase in computation as the input image resolution grows and shows excellent performance. However, the problem that aggregating global features from different windows is waiting to be resolved. Swin-Transformer is proposed to construct hierarchical encoding by a shifted-window mechanism to interactively learn the information between different windows. In this work, we investigate the outcome of applying an overlapped attention block (MoA) after the local attention layer and apply plenty to medical image segmentation tasks. The overlapped attention module employs slightly larger and overlapped patches in the key and value to enable neighbouring pixel information transmission, which leads to significant performance gain. The experimental results on the ACDC and Synapse datasets demonstrate that the used method performs better than previous Transformer models. © 2022 ACM.

关键词： image resolution

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Building a Stereo and Wide-View Hybrid RGB/FIR Imaging System for Autonomous Vehicle

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IEEE Sensors Journal 2022年第2期22卷 1638-1651页

作者： Zhang, Yigong Sun, Zezhou Wu, Banghe Yang, Jian Kong, Hui Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China University of Macau Taipa China

In this work, a novel paradigm is proposed to build a hybrid RGB/FIR camera, which outputs pixel-wisely aligned and temporally synchronized RGB and FIR frames simultaneously. Compared with the existing schemes, we innovate in several ways to make it compact, practical, and extendable for real-world applications. First, we propose a new structure layout design of the hybrid camera, and we also design a removable adjuster to detach the hybrid camera from the base platform. For RGB and FIR image registration, we have achieved a pixel-wise spatial registration of the FIR and RGB frames by a two-step alignment, where we propose a ghost-effect removal scheme to remove the ghost effect from the rolling-shutter-based FIR camera. Finally, multiple hybrid cameras are extended to a hybrid camera array, through which we can obtain wide-view spatially aligned FIR, RGB and disparity images simultaneously. The experimental results demonstrate the accuracy of the hybrid camera and the effectiveness of the designed stereo wide-view hybrid camera system. © 2001-2012 IEEE.

关键词： Cameras

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