Proceedings of the 4th International Conference on Control and Computer Vision最新文献_第3页

Region-Guided Pixel-Level Label Generation for Weakly Supervised Semantic Segmentation 弱监督语义分割的区域引导像素级标签生成

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484275

Xinyu Fu, Xiao Yao

{"title":"Region-Guided Pixel-Level Label Generation for Weakly Supervised Semantic Segmentation","authors":"Xinyu Fu, Xiao Yao","doi":"10.1145/3484274.3484275","DOIUrl":"https://doi.org/10.1145/3484274.3484275","url":null,"abstract":"The lack of reliable segmentation labels is the major obstacles to weakly supervised semantic segmentation. We provide a pseudo-label generation approach based on a deep convolutional neural network, which is supervised by the image-level category labels only. However, the limitation of the region of interest in the targets influences the effectiveness and integrity of the traditional methods in obtaining pixel-level mask annotations. This paper studies the characteristics of class activation mapping in classification network, focusing on the methods to enhance the localization ability of class activation mapping. We propose a Region-guided Pixel-label Generation framework (RPG) for semantic segmentation. The proposed region guidance mechanism decreases the influence of category supervision and makes use of the known high-level semantic information to guide the network, attaining more complete pixel-level annotations via expanding the regions of interest. Experimental results of training and validation on the PASCALVOC 2012 data set prove to achieve better pixel labeling and segmentation accuracy comparing with state-of-the-art methods.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121456354","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

Design Virtual Reality Simulation System for Epidemic (Covid-19) Education to Public 新型冠状病毒肺炎公众教育虚拟现实仿真系统设计

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484297

Zhanti Liang, Yongkang Xing, Kexin Guan, Zheng Da, Jianwen Fan, Gan Wu

{"title":"Design Virtual Reality Simulation System for Epidemic (Covid-19) Education to Public","authors":"Zhanti Liang, Yongkang Xing, Kexin Guan, Zheng Da, Jianwen Fan, Gan Wu","doi":"10.1145/3484274.3484297","DOIUrl":"https://doi.org/10.1145/3484274.3484297","url":null,"abstract":"In 2020, the COVID-19 epidemic swept the world and continued to spread. The global epidemic reflects the severe inadequacy of public education on infectious disease prevention in many countries. Therefore, it is necessary to popularize the knowledge of infectious disease prevention by designing a new epidemic education system. As a new interactive technology, Virtual Reality (VR) technology profoundly changes the human-computer interaction experience. According to the characteristics of VR, this research designed a virtual simulation system based on epidemic education research. This system is developed with Unreal 4 engine and simulates scenarios that require epidemic prevention education. The user interacts with the three-dimensional (3D) scene model through the VR controllers and visual user interface. This system allows users to effectively acquire knowledge about the epidemic and experience how medical staff deal with emergencies. This article discusses the methods of popularizing epidemic knowledge education by virtual simulation technology. It illustrates the system and knowledge popularization of the new epidemic health education in virtual simulation. The research will assist the public in acquiring epidemic knowledge effectively.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122822288","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

Active Disturbance Rejection Control for a Parabolic Trough Solar Field 抛物线槽型太阳场的自抗扰控制

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484299

Xian-hua Gao, Zhigang Su

引用次数: 1

Straw Defect Detection Algorithm Based on Pruned YOLOv3 基于修剪YOLOv3的秸秆缺陷检测算法

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484285

Qi-chang Xu, Liang Zhou

引用次数: 3

Learning from Human Uncertainty by Choquet Integral for Optic Disc Segmentation 基于人的不确定性学习的Choquet积分视盘分割

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484276

H. Qiu, P. Su, Shanshan Jiang, Xingyu Yue, Yitian Zhao, Jiang Liu

{"title":"Learning from Human Uncertainty by Choquet Integral for Optic Disc Segmentation","authors":"H. Qiu, P. Su, Shanshan Jiang, Xingyu Yue, Yitian Zhao, Jiang Liu","doi":"10.1145/3484274.3484276","DOIUrl":"https://doi.org/10.1145/3484274.3484276","url":null,"abstract":"Modern deep neural networks are able to beat human annotators in several medical image processing tasks. In practical manual annotation for medical image segmentation tasks, the labels of annotators often show inter-observer variability (IOV) which is mainly caused by annotators’ different understandings of expertise. In order to build a trustworthy segmentation system, robust models should consider how to capture uncertainty in samples and labels. Different from the conventional way of handling IOV with label fusion such as majority voting, a fuzzy integral based ensemble framework of multiple deep learning models for optic disc segmentation is proposed. Each component segmentation model is trained with respect to an annotator. Then, a powerful nonlinear aggregation function, the Choquet integral, is employed in form of a neural network to integrate the segmentation results of multiple annotators. The proposed method is validated on the public RIM-ONE dataset consisting of 169 fundus images and each image is annotated by 5 experts. Compared with conventional segmentation ensemble methods, the proposed methods achieves a higher Dice score (98.69%).","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131354883","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

Comparison between a Whole and Separated Feature Information for Acute Lymphoblastic Leukemia (ALL) Classification 整体与分离特征信息在急性淋巴细胞白血病(ALL)分类中的比较

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484289

A. Muntasa, Muhammad Yusuf

引用次数: 0

Multi-label Prediction for Visual Sentiment Analysis using Eight Different Emotions based on Psychology 基于心理学的八种不同情绪视觉情感分析多标签预测

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484296

Tetsuya Asakawa, Masaki Aono

{"title":"Multi-label Prediction for Visual Sentiment Analysis using Eight Different Emotions based on Psychology","authors":"Tetsuya Asakawa, Masaki Aono","doi":"10.1145/3484274.3484296","DOIUrl":"https://doi.org/10.1145/3484274.3484296","url":null,"abstract":"In visual sentiment analysis, sentiment estimation from images is a challenging research problem. Previous studies focused on a few specific sentiments and their intensities and have not captured abundant psychological human feelings. In addition, multi-label sentiment estimation from images has not been sufficiently investigated. The purpose of this research is to build a visual sentiment dataset, accurately estimate the sentiments as a multi-label multi-class problem from images that simultaneously evoke multiple emotions. We built a visual sentiment dataset based on Plutchik's wheel of emotions. We describe this ‘Senti8PW’ dataset, then perform multi-label sentiment analysis using the dataset, where we propose a combined deep neural network model that enables inputs from both hand-crafted features and CNN features. We also introduce a threshold-based multi-label prediction algorithm, in which we assume that each emotion has a probability distribution. In other words, after training our deep neural network, we predict evoked emotions for an image if the intensity of the emotion is larger than the threshold of the corresponding emotion. Extensive experiments were conducted on our dataset. Our model achieves superior results compared to the state-of-the-art algorithms in terms of subsets.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128159149","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

Fault Diagnosis of UAV System Base On One-Class Support Vector Machine 基于一类支持向量机的无人机系统故障诊断

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484301

Zaifei Fu, Xin Chen, Yu-juan Guo, Jing Chen

引用次数: 0

Improvement of Detection Rate for Small Objects Using Pre-processing Network 利用预处理网络提高小目标的检测率

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484283

Doohee Lee, Gi Soon Cha, Ehtesham Iqbal, H. Song, Kwang-nam Choi

{"title":"Improvement of Detection Rate for Small Objects Using Pre-processing Network","authors":"Doohee Lee, Gi Soon Cha, Ehtesham Iqbal, H. Song, Kwang-nam Choi","doi":"10.1145/3484274.3484283","DOIUrl":"https://doi.org/10.1145/3484274.3484283","url":null,"abstract":"Artificial intelligence (AI) has been developing in a variety of methods over the past decade. However most AI experts worried to build a deep or wide network because the accuracy of AI models depends heavily on the depth of the network. In general deep and wide networks are better at learning than those that are less deep and wide and wide. On the other hand deeper networks are more complex and have many disadvantages such as computational cost and system specification dependency. We propose a novel method to improve the average recall rate for small objects in the deep convolutional network in the paper. The proposed method added pre-processing layer before the network rather than stacking the networks deeper or wide. The presented pre-processing layer consists of two major parts: up-sampling and down-sampling of the data. The overall objective of up-sampling and down-sampling is to enhance the resolution of small objects in the input image. The pre-processing network improves the average recall rate of the base network to 3.56%. This experiment result depicts that the proposed method outperforms the small object detection performance. CCS CONCEPTS • Computing methodologies • Object detection","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120966421","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

Forecasting PM2.5 and Tracking Spatial Influence Patterns of Traffic Using Interpretable Deep Learning 利用可解释深度学习预测PM2.5和跟踪交通空间影响模式

Proceedings of the 4th International Conference on Control and Computer Vision Pub Date : 2021-08-13 DOI: 10.1145/3484274.3484302

Lianliang Chen, Z. Shan

{"title":"Forecasting PM2.5 and Tracking Spatial Influence Patterns of Traffic Using Interpretable Deep Learning","authors":"Lianliang Chen, Z. Shan","doi":"10.1145/3484274.3484302","DOIUrl":"https://doi.org/10.1145/3484274.3484302","url":null,"abstract":"Air pollution is a growing worldwide problem. Accurate prediction of PM2.5 concentration has a vital role to reduce the dramatic toll of air pollution on health. Due to the non-linearity and complexity of air pollution process and the influence of multiple factors, such as meteorological conditions, human activities and other chemical components, traditional pollution-related models have challenges in dealing with PM2.5 modeling. Based on atmospheric domain knowledge, we proposed a novel and interpretable deep learning model (iDeepAir) to predict hourly PM2.5 concentration by incorporating traffic data, meteorological data and air quality data. We designed feature interaction module and temporal interaction module to simulate pollution chemical reaction process and temporal accumulated process respectively, which makes the model has better understood and improves prediction accuracy of PM2.5 concentration. Compared to the best comparison model, mean absolute error (MAE) and rooted mean squared error (RMSE) were improved by 20.1% and 14.4% in 24h respectively. Furthermore, with the embedded Layerwise Relevance Propagation (LRP) algorithm, iDeepAir allows us to observe the spatial influence patterns of regional traffic emissions in a high-resolution way and evaluate the impact of traffic emissions on PM2.5 formation. Taking Shanghai as an example, we discover that although there are serious traffic emissions in some areas of Shanghai, they do not always directly aggravate air pollution, which is also affected by local buildings, meteorological conditions, and other human activities. These results show the spatial interpretability of our model and provide a quantitive decision-making basis for the government to control air pollution.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129220701","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