2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)最新文献_第6页

Absolute and Relative Pose Estimation in Refractive Multi View 折光多视图中绝对和相对姿态估计

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00290

Xiao Hu, F. Lauze, K. S. Pedersen, J. Mélou

引用次数: 5

An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration 基于移动监控机器人的异常检测系统

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00293

M. Zaheer, Arif Mahmood, M. H. Khan, M. Astrid, Seung-Ik Lee

{"title":"An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration","authors":"M. Zaheer, Arif Mahmood, M. H. Khan, M. Astrid, Seung-Ik Lee","doi":"10.1109/ICCVW54120.2021.00293","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00293","url":null,"abstract":"Autonomous anomaly detection is a fundamental step in visual surveillance systems, and so we have witnessed great progress in the form of various promising algorithms. Nonetheless, majority of prior algorithms assume static surveillance cameras that severely restricts the coverage of the system unless the number of cameras is exponentially increased, consequently increasing both the installation and the monitoring costs. In the current work we propose an anomaly detection system based on mobile surveillance cameras, i.e., moving robots which continuously navigate a target area. We compare the newly acquired test images with a database of normal images using geo-tags. For anomaly detection, a Siamese network is trained which analyses two input images for anomalies while ignoring the viewpoint differences. Further, our system is capable of updating the normal images database with human collaboration. Finally, we propose a new tester dataset that is captured by repeated visits of the robot over a constrained outdoor industrial target area. Our experiments demonstrate the effectiveness of the proposed system for anomaly detection using mobile surveillance robots.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130622697","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: 13

Boosting Fairness for Masked Face Recognition 提高蒙面人脸识别的公平性

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00178

Jun Yu, Xinlong Hao, Zeyu Cui, Peng He, Tongliang Liu

引用次数: 0

Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments 基于卷积神经网络的晴朗和多云环境遥感场景分类

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00085

Huiming Sun, Yuewei Lin, Q. Zou, Shaoyue Song, Jianwu Fang, Hongkai Yu

{"title":"Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments","authors":"Huiming Sun, Yuewei Lin, Q. Zou, Shaoyue Song, Jianwu Fang, Hongkai Yu","doi":"10.1109/ICCVW54120.2021.00085","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00085","url":null,"abstract":"Remote sensing (RS) scene classification has wide applications in the environmental monitoring and geological survey. In the real-world applications, the RS scene images taken by the satellite might have two scenarios: clear and cloudy environments. However, most of existing methods did not consider these two environments simultaneously. In this paper, we assume that the global and local features are discriminative in either clear or cloudy environments. Many existing Convolution Neural Networks (CNN) based models have made excellent achievements in the image classification, however they somewhat ignored the global and local features in their network structure. In this paper, we pro-pose a new CNN based network (named GLNet) with the Global Encoder and Local Encoder to extract the discriminative global and local features for the RS scene classification, where the constraints for inter-class dispersion and intra-class compactness are embedded in the GLNet training. The experimental results on two publicized RS scene classification datasets show that the proposed GLNet could achieve better performance based on many existing CNN backbones under both clear and cloudy environments.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121706748","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: 12

Fast Unsupervised MRI Reconstruction Without Fully-Sampled Ground Truth Data Using Generative Adversarial Networks 基于生成对抗网络的无监督核磁共振重建

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00444

Elizabeth K. Cole, Frank Ong, S. Vasanawala, J. Pauly

{"title":"Fast Unsupervised MRI Reconstruction Without Fully-Sampled Ground Truth Data Using Generative Adversarial Networks","authors":"Elizabeth K. Cole, Frank Ong, S. Vasanawala, J. Pauly","doi":"10.1109/ICCVW54120.2021.00444","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00444","url":null,"abstract":"Most deep learning (DL) magnetic resonance imaging (MRI) reconstruction approaches rely on supervised training algorithms, which require access to high-quality, fully-sampled ground truth datasets. In MRI, acquiring fully-sampled data is time-consuming, expensive, and, in some cases, impossible due to limitations on data acquisition speed. We present a DL framework for MRI reconstruction that does not require any fully-sampled data using unsupervised generative adversarial networks. We test our proposed method on 2D knee MRI data and 2D+time abdominal dynamic contrast enhanced (DCE) MRI data. In the DCE-MRI dataset, as is the case with many dynamic MRI sequences, ground truth was not possible to acquire and therefore, supervised DL reconstruction was not feasible. We show that our unsupervised method produces reconstructions which are better than compressed sensing in terms of image metrics and the recovery of anatomical structure, with faster inference time. In contrast to most deep learning reconstruction techniques, which are supervised, this method does not need any fully-sampled data. With the proposed method, accelerated imaging and accurate reconstruction can be performed in applications in cases where fully-sampled datasets are difficult to obtain or unavailable.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125276164","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: 14

3D mask presentation attack detection via high resolution face parts 3D面具呈现攻击检测通过高分辨率的面部部分

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00100

O. Grinchuk, Aleksandr Parkin, Evgenija Glazistova

引用次数: 4

An Algorithmic Approach to Quantifying GPS Trajectory Error 一种量化GPS轨迹误差的算法

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00437

Matthew Plaudis, Muhammad Azam, Derek Jacoby, M. Drouin, Y. Coady

引用次数: 3

A Computer Vision-Based Attention Generator using DQN 基于DQN的计算机视觉注意力生成器

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00329

Jordan B. Chipka, Shuqing Zeng, Thanura R. Elvitigala, P. Mudalige

引用次数: 0

Residual Dilated U-net For The Segmentation Of COVID-19 Infection From CT Images CT图像中COVID-19感染的残留扩张U-net分割

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00056

Alyaa Amer, Xujiong Ye, Faraz Janan

{"title":"Residual Dilated U-net For The Segmentation Of COVID-19 Infection From CT Images","authors":"Alyaa Amer, Xujiong Ye, Faraz Janan","doi":"10.1109/ICCVW54120.2021.00056","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00056","url":null,"abstract":"Medical imaging such as computed tomography (CT) plays a critical role in the global fight against COVID-19. Computer-aided platforms have emerged to help radiologists diagnose and track disease prognosis. In this paper, we introduce an automated deep-learning segmentation model, which builds upon the current U-net model, however, leverages the strengths of long and short skip connections. We complemented the long skip connections with a cascaded dilated convolution module that learns multi-scale context information, compensates the reduction in receptive fields, and reduces the disparity between encoded and decoded features. The short connections are considered in utilizing residual blocks as the basic building blocks for our model. They ease the training process, reduce the degradation problem, and propagate the low fine details. This enables the model to perform well in capturing smaller regions of interest. Furthermore, each residual block is followed by a squeeze and excitation unit, which stimulates informative features and suppresses less important ones, thus improving the overall feature representation. After extensive experimentation with a dataset of 1705 COVID-19 axial CT images, we demonstrate that performance gains can be achieved when deep learning modules are integrated with the basic U- net model. Experimental results show that our model outperformed the basic U-net and ResDUnet model by 8.1% and 1.9% in dice similarity, respectively. Our model provided a dice similarity measure of 85.3%, with a slight increase in trainable parameters, thus demonstrating a huge potential for use in the clinical domain.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131253533","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: 4

Mask Aware Network for Masked Face Recognition in the Wild 野外蒙面人脸识别的面具感知网络

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00168

Kai Wang, Shuo Wang, Jianfei Yang, Xiaobo Wang, Baigui Sun, Hao Li, Yang You

{"title":"Mask Aware Network for Masked Face Recognition in the Wild","authors":"Kai Wang, Shuo Wang, Jianfei Yang, Xiaobo Wang, Baigui Sun, Hao Li, Yang You","doi":"10.1109/ICCVW54120.2021.00168","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00168","url":null,"abstract":"Face recognition is one of the most important research topics for intelligence security system, especially in the COVID-19 era. Medical research has proven that wearing a mask is the most efficient way to avoid the risk of COVID-19. Nevertheless, classic face recognition systems often fail when dealing with masked faces. So it is essential to design a method that is robust to Masked Face Recognition (MFR). In this paper, to relieve the degraded performance of MFR, we propose Mask Aware Network (MAN) including a mask generation module and a loss function searching module. The mask generation module utilizes the face landmarks to obtain more realistic and reliable masked faces for training. The loss function searching module tries to match the most suitable loss for face recognition. On ICCV MFR challenge, our team victor-2021 achieves 5 first places (including 3 champions in standard face recognition and 2 champions in masked face recognition) and 1 third place by 3rd August 2021. These results demonstrate the robustness and generalization of our method in both standard or masked face recognition task.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133273557","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: 8