Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition最新文献_第4页

Metagenomic Sequence Classification based on One-Dimensional Convolutional Neural Network 基于一维卷积神经网络的宏基因组序列分类

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581835

Lei Xiao, Li Deng, Xiao Liu

引用次数: 0

From StarCraft II to Military Combat: The Framework of Auxiliary Decision System on Marine Warfare Based on Artificial Intelligence 从星际争霸II到军事作战:基于人工智能的海战辅助决策系统框架

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581895

Yi Sun, Ju Liu, Qing Sun

引用次数: 0

General Algorithms to Solve Nonlinear Algebraic Equations with C++ 用c++求解非线性代数方程的一般算法

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581886

J. Lieh

引用次数: 0

A Method of Spacecraft Orbit Anomaly Discrimination Based on Long Short-Term Memory Network 基于长短期记忆网络的航天器轨道异常判别方法

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581891

Zonghua Qu, Chunling Wei, Han Yan

引用次数: 0

MoAFormer: Aggregating Adjacent Window Features into Local Vision Transformer Using Overlapped Attention Mechanism for Volumetric Medical Segmentation MoAFormer:利用重叠注意机制将相邻窗口特征聚合成局部视觉变换，用于体积医学分割

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581825

Yixi Luo, Huayi Yin, X. Du

引用次数: 0

HRPM Net: An Efficient Feature Learning Network from The Biological Modelling Of Human Retinal Perception Mechanism HRPM网络:基于人眼视网膜感知机制生物学建模的高效特征学习网络

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581869

Renwei Ba, Yidan Zhang, Zhenghui Hu, Jun Sun, Xiao Li

{"title":"HRPM Net: An Efficient Feature Learning Network from The Biological Modelling Of Human Retinal Perception Mechanism","authors":"Renwei Ba, Yidan Zhang, Zhenghui Hu, Jun Sun, Xiao Li","doi":"10.1145/3581807.3581869","DOIUrl":"https://doi.org/10.1145/3581807.3581869","url":null,"abstract":"The biological model of the mammal visual mechanisms is very beneficial to feature learning in motionless images. It is proved that the visual mechanisms can improve the performance of the hand-crafted methods and CNNS method. Recently CNNs learn discriminate and robust features by changing the backbone, processing multi-scale feature maps, and adding attention mechanisms, etc. While they are relatively short of changing the network structure with human retina mechanisms, which have been proven to have a strong feature extract capability of images by traditional feature descriptors. To address this problem, we present two CNN blocks, multi-scale receptive field convolutional block (MSRF) and Sensitivity block (SENSI), both of which are constructed by modeling the human retina ganglion cell's mechanisms. MSRF is designed to enhance the feature discriminability and robustness by imitating the exponentially increased way of the receptive fields of the P ganglion cells in the human retina. We constructed experiments to get the specific value of the size of the receptive fields, and it can capture both local and global features with various convolution kernels. SENSI is presented to make sure each receptive field has a suitable weight to choose which receptive field can better learn features. Both of them help to learn features and can be easily integrated into the existing CNN models. The framework is evaluated on two benchmark datasets. We further assemble MSRF and SENSI to the top of SSD, constructing the HRPM Net. The model outperforms the state-of-the-art approaches by a considerable margin on MS COCO, VOC 2012, and VOC 2007 datasets. The results also show that MSRF block and SENSI block are helpful in feature learning and can improve the performance by a margin.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114976549","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

Progresses in Link Prediction: A Survey 链接预测研究进展综述

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581903

Jiahao Li, Linlan Liu, Jian Shu

引用次数: 0

Artificial Neural Network-assisted Amplitude Thresholding Improves Spike Detection 人工神经网络辅助振幅阈值法改进了尖峰检测

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581875

Xiang Cheng, Xuan Han, Yu Song, Tielin Zhang, Bo Xu

引用次数: 0

Drivable Area Segmentation in Unstructured Roads for Autonomous Vehicles based on Multi-sensor Fusion 基于多传感器融合的自动驾驶非结构化道路可行驶区域分割

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581828

Hanqi Wang, Huawei Liang, L. Chen, Diancheng Gong, Pengfei Zhou, Bin Kong

引用次数: 0

CNN-based EEG Classification Method for Drug Use Detection 基于cnn的脑电分类方法用于药物使用检测

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581868

Hui Zeng, Banghua Yang, Xuelin Gu, Yongcong Li, Xinxing Xia, Shouwei Gao

{"title":"CNN-based EEG Classification Method for Drug Use Detection","authors":"Hui Zeng, Banghua Yang, Xuelin Gu, Yongcong Li, Xinxing Xia, Shouwei Gao","doi":"10.1145/3581807.3581868","DOIUrl":"https://doi.org/10.1145/3581807.3581868","url":null,"abstract":"Common methods to detect whether a person uses drugs require taking biological samples of the subject, which have time limitation due to the samples. To avoid this, this paper proposes a CNN-based EEG classification method for drug use detection, which does not require taking biological samples of the subject and can trace a longer drug use history of the subject. In this paper, a convolutional neural network-based EEG classification algorithm incorporating batch normalization after the convolutional layer and also introducing dropout operation in the fully connected layer to speed up the training process is designed to distinguish between healthy controls and drug addicts, which reduces the sensitivity of parameters, effectively mitigates the occurrence of overfitting and improves the accuracy compared to traditional machine learning algorithms. Data were collected from eight healthy controls and eight drug addicts. The algorithm obtained the classification accuracy of 85.46% using eight-fold cross-validation. The result of classification shows that the method is an effective way to detect whether the examined person is drug addict, which can easier bring hidden drug addicts under control and reduce the social harm caused by drugs.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125563219","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