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A VERSION OF ISOMAP WITH EXPLICIT MAPPING

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A VERSION OF ISOMAP WITH EXPLICIT MAPPING

2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： CHUN-GUANG LI JUN GUO GUANG CHEN XIANG-FEI NIE ZHEN YANG Pattern Recognition and Intelligent System Lab School of Information Engineering Beijing University of Posts and Telecommunications Beijing 100876 China

Recently several manifold learning algorithms have been presented for nonlinear dimensionality reduction. Isomap is one of them. However, Isomap suffers from a deficiency that it does not give an explicit mapping function, which is from high dimensional space to low dimensional target space. In this paper, a version of Isomap with explicit mapping, called E-Isomap, is proposed. In E-Isomap, the geodesic distance matrix is fed into a cost function and then Iterative Majorization is adopted to solve an optimization problem for obtaining both the low dimensional configuration and the nonlinear mapping. Owing to the existence of explicit mapping, this version of Isomap can be more easily used in pattern recognition than the original ones. The experiments on two benchmark data sets are given to demonstrate the performance of the presented method.

关键词： Nonlinear Dimensionality Reduction Manifold Learning Isomap Geodesic Distance E-Isomap

Real-time Lesion Detection of Cardiac Coronary Artery Using Deep Neural Networks

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Real-time Lesion Detection of Cardiac Coronary Artery Using ...

IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Tianming Du Xuqing Liu Honggang Zhang Bo Xu Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing China CIT Chinese Academy of Medical Sciences

In the field of cardiac arterial interventional therapy, coronary angiography imaging provides key information to physicians for treatment strategy selection, while the lesion identification process is time-consuming and error-prone even for experienced doctors. This paper proposes a method for the automatic detection of lesion in cardiac coronary angiography based on the deep learning and convolution neural network for the very first time. We used 2925 medical images for building the model. Several lesions exist on the vessel of each image. We will regard these lesion areas as objects that are different from other background areas. We designed a model based on the convolution neural network, applying some advanced building block including CReLU, Inception and other advanced technology such as batch normalization, residual connections, skip-layer connection in our network model. After training, the network model can distinguish the difference between a lesion area and a normal vessel area (background), which can detect the location of the coronary artery lesion in real time without any manual intervention. For the stenosis lesion, the recall rate of detection achieves 0.88.

关键词： Lesions Arteries Convolution Training Task analysis Biomedical imaging

The Architecture of Centralized Access Control Proxy Server based on NETCONF

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The Architecture of Centralized Access Control Proxy Server ...

2010 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2010)(2010年检测技术与机电自动化国际会议)

作者： Feng Wang Bin Zhang Guohui Li Jun Guo Xuesong Gao Yan Li Pattern Recognition and Intelligent System Lab Beijing University of Post and Telecommunications Bei HuaWei Beijing Research Institute Beijing China

ISBN: (纸本)9781424450015;9780769539621

NETCONF is a new protocol as the network becomes more and more complexity, which has a better capability administering lots of devices. However, the large scale network brings some trouble for implementing access control especially when administers need to deploy or update policies on each devices, in this regard, the architecture of centralized access control proxy server based on NETCONF is focused, which realizes policies management and authorization centralized and raises administration efficiency. In order to figure it out absolutely, we present the basic theory giving the detailed illustration about components of the ***, we describe how dose the architecture work based on NETCONF.

关键词： NETCONF Access Control Centralized Architecture

Tlrm: Task-level relation module for GNN-based few-shot learning

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arXiv 2021年

作者： Guo, Yurong Ma, Zhanyu Li, Xiaoxu Dong, Yuan Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing China Lanzhou University of Technology Lanzhou China

Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited lab.led samples per class. GNN-based few-shot learning architectures mostly replace traditional metric with a learnable GNN. In the GNN, the nodes are set as the samples’ embedding, and the relationship between two connected nodes can be obtained by a network, the input of which is the difference of their embedding features. We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks. That is, this method of measuring relation does not take the task-level information into account. To this end, we propose a new relation measure method, namely the task-level relation module (TLRM), to explicitly model the task-level relation of one sample to all the others. The proposed module captures the relation representations between nodes by considering the sample-to-task instead of sample-to-sample embedding features. We conducted extensive experiments on four benchmark datasets: mini-ImageNet, tiered-ImageNet, CUB-200-2011, and CIFAR-FS. Experimental results demonstrate that the proposed module is effective for GNN-based few-shot learning. © 2021, CC BY.

关键词： Graph neural networks

GPCA: A probabilistic framework for Gaussian process embedded channel attention

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arXiv 2020年

作者： Xie, Jiyang Ma, Zhanyu Chang, Dongliang Zhang, Guoqiang Guo, Jun Pattern Recognition and Intelligent System Lab. School of Artificial Intelligence Beijing University of Posts and Telecommunications China School of Electrical and Data Engineering University of Technology Sydney Australia

Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention modules have been proposed and implemented in various ways. Generally speaking, they are mainly based on convolution and pooling operations. In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way. The GPCA module intends to model the correlations among the channels, which are assumed to be captured by beta distributed variables. As the beta distribution cannot be integrated into the end-to-end training of convolutional neural networks (CNNs) with a mathematically tractable solution, we utilize an approximation of the beta distribution to solve this problem. To specify, we adapt a Sigmoid-Gaussian approximation, in which the Gaussian distributed variables are transferred into the interval [0, 1]. The Gaussian process is then utilized to model the correlations among different channels. In this case, a mathematically tractable solution is derived. The GPCA module can be efficiently implemented and integrated into the end-to-end training of the CNNs. Experimental results demonstrate the promising performance of the proposed GPCA module. Codes are availab.e at https://***/PRIS-CV/GPCA. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

TLRM: Task-level Relation Module for GNN-based Few-Shot Learning

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TLRM: Task-level Relation Module for GNN-based Few-Shot Lear...

IEEE Visual Communications and Image Processing (VCIP)

作者： Yurong Guo Zhanyu Ma Xiaoxu Li Yuan Dong Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China Beijing Academy of Artificial Intelligence Beijing China Lanzhou University of Technology Lanzhou China

ISBN: (纸本)9781728173221

Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited lab.led samples per class. GNN-based few-shot learning architectures mostly replace traditional metric with a learnable GNN. In the GNN, the nodes are set as the samples' embedding, and the relationship between two connected nodes can be obtained by a network, the input of which is the difference of their embedding features. We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks. That is, this method of measuring relation does not take the task-level information into account. To this end, we propose a new relation measure method, namely the task-level relation module (TLRM), to explicitly model the task-level relation of one sample to all the others. The proposed module captures the relation representations between nodes by considering the sample-to-task instead of sample-to-sample embedding features. We conducted extensive experiments on four benchmark datasets: mini-ImageNet, tiered-ImageNet, CUB-200-2011, and CIFAR-FS. Experimental results demonstrate that the proposed module is effective for GNN-based few-shot learning.

关键词： Measurement Visual communication Image processing Benchmark testing Graph neural networks Task analysis

Progressive co-attention network for fine-grained visual classification

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arXiv 2021年

作者： Zhang, Tian Chang, Dongliang Ma, Zhanyu Guo, Jun Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing China Beijing Academy of Artificial Intelligence Beijing China

Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take an individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features. Considering that complementary information is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft. © 2021, CC BY.

关键词： Classification (of information)

FICAL: Focal Inter-Class Angular Loss for Image Classification

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FICAL: Focal Inter-Class Angular Loss for Image Classificati...

IEEE Visual Communications and Image Processing Conference

作者： Xinran Wei Dongliang Chang Jiyang Xie Yixiao Zheng Chen Gong Chuang Zhang Zhanyu Ma Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications Beijing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education

Convolutional Neural Networks (CNNs) have been successfully applied in various image analysis tasks and gradually become one of the most powerful machine learning approaches. In order to improve the capability of the model generalization and performance in image classification, a new trend is to learn more discriminative features via CNNs. The main contribution of this paper is to increase the angles between the categories to extract discriminative features and enlarge the inter-class variance. To this end, we propose a loss function named focal inter-class angular loss (FICAL) which introduces the confusion rate-weighted cosine distance as the similarity measurement between categories. This measurement is dynamically evaluated during each iteration to adapt the model. Compared with other loss functions, experimental results demonstrate that the proposed FICAL achieved best performance among the referred loss functions on two image classification datasets.

关键词： Task analysis Training Feature extraction Computer vision Computational modeling Loss measurement Neural networks

Meta Balanced Network for Fair Face recognition

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arXiv 2022年

作者： Wang, Mei Zhang, Yaobin Deng, Weihong The Pattern Recognition and Intelligent System Lab. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Key Lab. of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

Although deep face recognition has achieved impressive progress in recent years, controversy has arisen regarding discrimination based on skin tone, questioning their deployment into real-world scenarios. In this paper, we aim to systematically and scientifically study this bias from both data and algorithm aspects. First, using the dermatologist approved Fitzpatrick Skin Type classification system and Individual Typology Angle, we contribute a benchmark called Identity Shades (IDS) database, which effectively quantifies the degree of the bias with respect to skin tone in existing face recognition algorithms and commercial APIs. Further, we provide two skin-tone aware training datasets, called BUPT-Globalface dataset and BUPT-Balancedface dataset, to remove bias in training data. Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones. To determine the margins, our method optimizes a meta skewness loss on a clean and unbiased meta set and utilizes backward-on-backward automatic differentiation to perform a second order gradient descent step on the current margins. Extensive experiments show that MBN successfully mitigates bias and learns more balanced performance for people with different skin tones in face recognition. The proposed datasets are availab.e at http://***/RFW/***. © 2022, CC BY.

关键词： Face recognition