检索结果-内蒙古大学图书馆

8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

作者： Zhou, Cheng Li, Zhengquan Wu, Qiong Liu, Yang Li, Baolong Wu, Guilu Zhao, Xiaoqing Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China National Mobile Communication Research Laboratory Southeast University Nanjing210096 China Department of Electronic Engineering Tsinghua University Beijing100084 China The National Key Laboratory of millimeter wave Southeast University Nanjing210096 China

ISBN: (纸本)9789811394089

With the boost of the quantity of antennas at the base station (BS) in massive multiple-input multiple-output (MIMO) systems, the channel capacity and spectral efficiency are also increased. Conventional channel estimation method, such as the classical minimum mean square error (MMSE), which involves the matrix inversion in large size with enormous computational complexity, especially in massive MIMO systems due to large antenna arrays. To degrade the complexity caused by the inversion of the matrix, a low-complexity channel estimation scheme is proposed based on the improved symmetric successive over relaxation preconditioned conjugate gradient (ISSOR-PCG) method to avoid computing the matrix inversion directly. A simple way is also introduced to address the optimal relaxation parameter for the proposed scheme, by utilizing the channel asymptotic orthogonality in massive MIMO systems. Analysis shows that the proposed channel estimator is able to degrade the complexity effectively compared with MMSE channel estimator. Simulation results illustrate that the proposed scheme can obtain near-optimal performance to the classical MMSE estimation method and outperforms other baseline schemes with increased number of iterations. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

A Deep Decomposition Network for Image Processing: A Case Study of Image Fusion

SSRN

引用

SSRN 2022年

作者： Fu, Yu Xu, Tianyang Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Image decomposition into constituent components has many applications in the field of image processing. It aims to extract salient features from the source image for subsequent pattern recognition. In this paper, we propose a new image decomposition method based on a convolutional neural network. It is applied to the task of image fusion. In particular, a pair of infrared and visible light images are decomposed into three high-frequency feature images and a low-frequency feature images respectively. The two sets of feature images are fused using a novel fusion strategy to obtain fused feature maps. The feature maps are subsequently reconstructed to obtain the fused image. Compared with the state-of-the-art fusion methods, the proposed approach achieves betterperformance in both subjective and objective evaluation. © 2022, The Authors. All rights reserved.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion

arXiv

引用

arXiv 2023年

作者： Xiao, Yongbiao Li, Hui Cheng, Chunyang Song, Xiaoning Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this end, we propose a fusion network based on the local edge enhancement, named LE2Fusion. Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image. For feature extraction, a multi-scale residual attention (MRA) module is applied to extract rich features. Then, with LE2, a set of enhancement weights are generated which are utilized in feature fusion strategy and used to guide the image reconstruction. To better preserve the local detail information and structure information, the pixel intensity loss function based on the local region is also presented. The experiments demonstrate that the proposed method exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Mutual Information-Guided GA for Bayesian Network Structure Learning (Extended Abstract)

Mutual Information-Guided GA for Bayesian Network Structure ...

引用

International Conference on Data engineering

作者： Kefei Yan Wei Fang Hengyang Lu Xin Zhang Jun Sun Xiaojun Wu Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy.

关键词： Accuracy NP-hard problem Sociology Search problems Bayes methods Statistics Optimization

来源：评论

学校读者我要写书评

暂无评论

Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action recognition

Spatial Residual Layer and Dense Connection Block Enhanced S...

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Cong Wu Xiao-Jun Wu Josef Kittler School of IOT Engineering Jiangnan University China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence CVSSP University of Surrey UK

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Recent research has shown that modeling the dynamic joint features of the human body by a graph convolutional network (GCN) is a groundbreaking approach for skeleton-based action recognition, especially for the recognition of the body-motion, human-object and human-human interactions. Nevertheless, how to model and utilize coherent skeleton information comprehensively is still an open problem. In order to capture the rich spatiotemporal information and utilize features more effectively, we introduce a spatial residual layer and a dense connection block enhanced spatial temporal graph convolutional network. More specifically, our work introduces three aspects. Firstly, we extend spatial graph convolution to spatial temporal graph convolution of cross-domain residual to extract more precise and informative spatiotemporal feature, and reduce the training complexity by feature fusion in the, so-called, spatial residual layer. Secondly, instead of simply superimposing multiple similar layers, we use dense connection to take full advantage of the global information. Thirdly, we combine the above mentioned two components to create a spatial temporal graph convolutional network (ST-GCN), referred to as SDGCN. The proposed graph representation has a new structure. We perform extensive experiments on two large datasets: Kinetics and NTU-RGB+D. Our method achieves a great improvement in performance compared to the mainstream methods. We evaluate our method quantitatively and qualitatively, thus proving its effectiveness.

关键词： Convolution Spatiotemporal phenomena Joints Feature extraction Two dimensional displays Three-dimensional displays

来源：评论

学校读者我要写书评

暂无评论

CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection

arXiv

引用

arXiv 2023年

作者： Wang, Shunfeng Li, Yueyang Luo, Haichi Bi, Chenyang Faculty of Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiang-nan University 1800 Lihu Avenue Jiangsu Wuxi214122 China

In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples. Moreover, among numerous deep learning methods, supervised methods generally exhibit superior performance compared to unsupervised methods. Considering the reasons mentioned above, we propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow to achieve more precise detection outcomes and expedited inference processes. On one hand, we introduce a novel approach to anomaly synthesis, yielding anomalous samples in accordance with authentic industrial scenarios, alongside their surrogate annotations. On the other hand, having obtained a substantial number of anomalous samples, we enhance the 2D-Flow framework by incorporating contrastive learning, leveraging diverse proxy tasks to fine-tune the network. Our approach enables the network to learn more precise mapping relationships from self-generated labels while retaining the lightweight characteristics of the 2D-Flow. Compared to mainstream unsupervised approaches, our self-supervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Furthermore, the entire training and inference process is end-to-end. Our approach showcases new state-of-the-art results, achieving a performance of 99.6% in image-level AUROC on the MVTecAD dataset and 96.8% in image-level AUROC on the BTAD dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Label distribution expression recognition algorithm based on asymptotic truth value

引用

Journal of Measurement Science and Instrumentation 2021年第3期12卷 295-303页

作者： HUANG Hao GE Hongwei School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi 214122 China

Ambiguous expression is a common phenomenon in facial expression recognition(FER).Because of the existence of ambiguous expression,the effect of FER is severely *** reason maybe that the single label of the data cannot effectively describe complex emotional intentions which are vital in *** distribution learning contains more information and is a possible way to solve this *** apply label distribution learning on FER,a label distribution expression recognition algorithm based on asymptotic truth value is *** the premise of not incorporating extraneous quantitative information,the original information of database is fully used to complete the generation and utilization of label ***,in training part,single label learning is used to collect the mean value of the overall distribution of ***,the true value of data label is approached gradually on the granularity of data ***,the whole network model is retrained using the generated label distribution *** results show that this method can improve the accuracy of the network model obviously,and has certain competitiveness compared with the advanced algorithms.

关键词： facial expression recognition(FER) label distributed learning label smoothing ambiguous expression

来源：评论

学校读者我要写书评

暂无评论

Collaborative Representation for SPD Matrices with Application to Image-Set Classification

arXiv

引用

arXiv 2022年

作者： Chu, Li Wang, Rui Wu, Xiao-Jun The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University China

Collaborative representation-based classification (CRC) has demonstrated remarkable progress in the past few years because of its closed-form analytical solutions. However, the existing CRC methods are incapable of processing the nonlinear variational information directly. Recent advances illustrate that how to effectively model these nonlinear variational information and learn invariant representations is an open challenge in the community of computer vision and pattern recognition To this end, we try to design a new algorithm to handle this problem. Firstly, the second-order statistic, i.e., covariance matrix is applied to model the original image sets. Due to the space formed by a set of nonsingular covariance matrices is a well-known Symmetric Positive Definite (SPD) manifold, generalising the Euclidean collaborative representation to the SPD manifold is not an easy task. Then, we devise two strategies to cope with this issue. One attempts to embed the SPD manifold-valued data representations into an associated tangent space via the matrix logarithm map. Another is to embed them into a Reproducing Kernel Hilbert Space (RKHS) by utilizing the Riemannian kernel function. After these two treatments, CRC is applicable to the SPD manifold-valued features. The evaluations on four banchmarking datasets justify its effectiveness. © 2022, CC BY.

关键词： Covariance matrix

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Knowledge Transfer Network for Distantly Supervised Relation Extraction

Hierarchical Knowledge Transfer Network for Distantly Superv...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wei Song Weishuai Gu School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

Distantly supervised relation extraction (DSRE) aims to identify the relation between the two entities (e.g. name and location). Most existing methods extract semantic features from each level separately, without taking into account the transfer of hierarchical knowledge obtained at various levels. As a result, a large amount of knowledge that can improve the quality of the feature representations is lost, resulting in decreased performance for predicting entity relations. In this paper, we propose a novel framework termed the Hierarchical Knowledge Transfer Network (HKTN) that is capable of transferring hierarchical knowledge learned from different levels to improve the performance of predicting entity relations. Specifically, the two representation refinement blocks with re-calibrators at the bag and group levels construct robust bag features and comprehensive group features, respectively. During the construction process, the high-level features are capable of guiding the learning of the bottom-level features using the two re-calibrators. As the construction of the high-level feature representations is based on the bottom-level feature representations, prediction-based contrastive learning fully excavates bottom-level features, which can improve the quality of the feature representation at each level. The experimental results demonstrate that our proposed HKTN achieves an obvious improvement on the two benchmark datasets, including NYT-10 and GDS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

General and robust voxel feature learning with Transformer for 3D object detection

引用

Journal of Measurement Science and Instrumentation 2022年第1期13卷 51-60页

作者： LI Yang GE Hongwei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi 214122 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China

The self-attention networks and Transformer have dominated machine translation and natural language processing fields,and shown great potential in image vision tasks such as image classification and object *** by the great progress of Transformer,we propose a novel general and robust voxel feature encoder for 3D object detection based on the traditional *** first investigate the permutation invariance of sequence data of the self-attention and apply it to point cloud *** we construct a voxel feature layer based on the self-attention to adaptively learn local and robust context of a voxel according to the spatial relationship and context information exchanging between all points within the ***,we construct a general voxel feature learning framework with the voxel feature layer as the core for 3D object *** voxel feature with Transformer(VFT)can be plugged into any other voxel-based 3D object detection framework easily,and serves as the backbone for voxel feature *** results on the KITTI dataset demonstrate that our method achieves the state-of-the-art performance on 3D object detection.

关键词： 3D object detection self-attention networks voxel feature with Transformer(VFT) point cloud encoder-decoder

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：