2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)最新文献_第2页

Adaptively Discriminant Locality Preserving Projection 自适应判别保局域投影

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00119

Zipei Chen

引用次数: 0

Adversarial Learning Based on Global and Local Features for Cross-Modal Person Re-identification 基于全局和局部特征的对抗学习跨模态人物再识别

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00047

Zizhen Shuai, Shuaishuai Li, Yang Gao, Fei Wu

{"title":"Adversarial Learning Based on Global and Local Features for Cross-Modal Person Re-identification","authors":"Zizhen Shuai, Shuaishuai Li, Yang Gao, Fei Wu","doi":"10.1109/icaice54393.2021.00047","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00047","url":null,"abstract":"In recent years, a great improvement has been achieved in cross-modal person re-identification (Re-ID) methods based on feature partition. However, many works do not use global and local features jointly to improve the accuracy of person identification. It is an important research topic to fully extract and use global features as well as local features, and effectively reduce modality differences. In this paper, we propose an adversarial learning based on global and local features (ALGL) method. We adopt a two-stream network with partially shared parameters as a feature extraction network to extract visible and infrared feature maps. Local features are obtained through Part-based Convolutional Baseline (PCB) operations on feature maps with the local feature learning module. In the global feature learning module, the average pooling is used to obtain the global features. In order to fully explore the discriminative abilities of local features and global features, hetero-center based triplet loss is designed, which brings features of the same category closer, and features of different categories farther away. At the same time, the adversarial learning module minimizes the modality difference between visible and infrared modalities. Experimental results on the SYSU-MM01 and RegDB datasets show that ALGL outperforms the state-of-the-art solutions.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134340519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Judgement Model of the Traditional Chinese Medicine's Quality based on Ensemble Algorithms 基于集成算法的中药质量评判模型

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00105

Li Xiao, Peng Jiao, Guanyu Chen

引用次数: 0

Autonomous navigation for indoor mobile robots based on reinforcement learning 基于强化学习的室内移动机器人自主导航

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00056

Lun Ge, Xiaoguang Zhou, Chi Zhang

引用次数: 0

A firefly optimization fault-tolerant control algorithm for 4500m human occupied vehicle 4500米载人车辆萤火虫优化容错控制算法

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00017

Xiaotong Yan, Daqi Zhu, W. Zhou

引用次数: 0

Weighted Deformable Convolution Network with IOU-boundary loss for Solid Waste Detection 具有ou边界损失的加权可变形卷积网络用于固体废物检测

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00084

Beibei Zhao, Xiong Xu

引用次数: 0

An Intelligent Evaluation Method of Design Scheme for Electromagnet Quality Based on ELECTRE II 基于ELECTREⅱ的电磁铁质量设计方案智能评价方法

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00076

Fasong Chen, J. Pang

引用次数: 1

Generalized Power Flow Algorithm for Multi-energy Flow New Distribution Network

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00039

Qiyou Lin, X. Shu, Xiaodie Niu, Yanbin Chen, Cheng Yin, Dan Zhou

引用次数: 0

Classification of Lung Nodule Malignancy on CT Images Using Convolutional Neural Network 基于卷积神经网络的肺结节恶性肿瘤CT图像分类

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00093

Wenbin Su, Qianxue Jiang, Yanchen Jing, Xiaorun Zhu

引用次数: 0

A General Multi Time Scale Spatiotemporal Compound Model for EEG Classification 一种通用的多时间尺度时空复合脑电分类模型

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00080

Renxiang Chen, Xiaohong Liu, Wenli Dai, Yao Gao

{"title":"A General Multi Time Scale Spatiotemporal Compound Model for EEG Classification","authors":"Renxiang Chen, Xiaohong Liu, Wenli Dai, Yao Gao","doi":"10.1109/icaice54393.2021.00080","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00080","url":null,"abstract":"Recently Brain computer interfaces (BCI), a direct communication technique between machine and brain, plays an important role in the fields of brain disease diagnose, rehabilitation and robotics with the help of electroencephalography (EEG). EEG-based brain signal feature extraction and task categorization have become a popular trend. The procedure of brain signal analysis includes three steps: pre-process, feature extraction and categorization. For a given BCI paradigm, these steps are tailored to explore distinct characteristics of its expected control signals. To generalize the process, we propose a multi time scale spatiotemporal compound classification model (MTSC) based on Convolution Neural Network (CNN). The model firstly utilizes 2D convolution along time axis capturing temporal feature, then depth-wise convolution along channel axis is done for capturing spatial feature. Both of two domain features composite a facet of original EEG signal. We set up different 2D convolution kernel size according to the signal sample rate in order to generate different views, which contains various time scale information. These views are weighted summed for classification. Experiments on four different paradigm datasets have been conducted comparing with well performed deep learning and traditional methods. The results show that our model achieves better marks on all datasets in accuracy and F1-score.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125428052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0