2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW)最新文献

Facial Expression Neutralization With StoicNet 面部表情中和StoicNet

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00026

W. Carver, Ifeoma Nwogu

引用次数: 2

Using Semantic Information to Improve Generalization of Reinforcement Learning Policies for Autonomous Driving 利用语义信息改进自动驾驶强化学习策略的泛化

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00020

Florence Carton, David Filliat, Jaonary Rabarisoa, Q. Pham

引用次数: 2

Reliability of GAN Generated Data to Train and Validate Perception Systems for Autonomous Vehicles GAN生成数据在自动驾驶汽车感知系统训练和验证中的可靠性

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00023

Weihuang Xu, Nasim Souly, P. Brahma

引用次数: 6

DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder 基于深度时空卷积自编码器的自动驾驶系统鲁棒化

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00016

A. Papachristodoulou, C. Kyrkou, T. Theocharides

{"title":"DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder","authors":"A. Papachristodoulou, C. Kyrkou, T. Theocharides","doi":"10.1109/WACVW52041.2021.00016","DOIUrl":"https://doi.org/10.1109/WACVW52041.2021.00016","url":null,"abstract":"Autonomous vehicles increasingly rely on cameras to provide the input for perception and scene understanding and the ability of these models to classify their environment and objects, under adverse conditions and image noise is crucial. When the input is, either unintentionally or through targeted attacks, deteriorated, the reliability of autonomous vehicle is compromised. In order to mitigate such phenomena, we propose DriveGuard, a lightweight spatio-temporal autoencoder, as a solution to robustify the image segmentation process for autonomous vehicles. By first processing camera images with DriveGuard, we offer a more universal solution than having to re-train each perception model with noisy input. We explore the space of different autoencoder architectures and evaluate them on a diverse dataset created with real and synthetic images demonstrating that by exploiting spatio-temporal information combined with multi-component loss we significantly increase robustness against adverse image effects reaching within 5-6% of that of the original model on clean images.","PeriodicalId":313062,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134394796","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: 1

Explainable Fingerprint ROI Segmentation Using Monte Carlo Dropout 可解释的指纹ROI分割使用蒙特卡罗Dropout

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00011

Indu Joshi, R. Kothari, Ayush Utkarsh, V. Kurmi, A. Dantcheva, Sumantra Dutta Roy, P. Kalra

引用次数: 11

2020 Sequestered Data Evaluation for Known Activities in Extended Video: Summary and Results 扩展视频中已知活动的隔离数据评估:总结和结果

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00010

A. Godil, Yooyoung Lee, J. Fiscus, Andrew Delgado, Eliot Godard, Baptiste Chocot, Lukas L. Diduch, Jim Golden, Jesse Zhang

{"title":"2020 Sequestered Data Evaluation for Known Activities in Extended Video: Summary and Results","authors":"A. Godil, Yooyoung Lee, J. Fiscus, Andrew Delgado, Eliot Godard, Baptiste Chocot, Lukas L. Diduch, Jim Golden, Jesse Zhang","doi":"10.1109/WACVW52041.2021.00010","DOIUrl":"https://doi.org/10.1109/WACVW52041.2021.00010","url":null,"abstract":"This paper presents a summary and results for the ActEV’20 SDL (Activities in Extended Video Sequestered Data Leaderboard) challenge that was held under the CVPR’20 ActivityNet workshop [38]. The primary goal of the challenge was to provide an impetus for advancing research and capabilities in the field of human activity detection in untrimmed multi-camera videos. Advancements in activity detection will help with a wide range of public safety applications. The challenge was administered by the National Institute of Standards and Technology (NIST), where anyone could submit their system which run on sequestered data with the resulting score posted to a public leaderboard. Ten teams submitted their systems for the ActEV’20 SDL competition on the Multiview Extended Video with Activities (MEVA) test set with 37 target activities. The performance metric for the leaderboard ranking is the partial, normalized Area Under the Detection Error Tradeoff (DET) curve (nAUDC). The top rank on activity detection was by UCF at 37%, followed by CMU at 39% and OPPO at 41%.","PeriodicalId":313062,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"7 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130850815","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

Geeks and guests: Estimating player’s level of experience from board game behaviors 极客和客人:从桌游行为中评估玩家的体验水平

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00007

Feyisayo Olalere, Metehan Doyran, R. Poppe, A. A. Salah

引用次数: 2

Weakly Supervised Multi-Object Tracking and Segmentation 弱监督多目标跟踪与分割

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00018

Idoia Ruiz, L. Porzi, S. R. Bulò, P. Kontschieder, J. Serrat

引用次数: 7

An Explainable Attention-Guided Iris Presentation Attack Detector 一个可解释的注意力引导虹膜呈现攻击检测器

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00015

Cunjian Chen, A. Ross

{"title":"An Explainable Attention-Guided Iris Presentation Attack Detector","authors":"Cunjian Chen, A. Ross","doi":"10.1109/WACVW52041.2021.00015","DOIUrl":"https://doi.org/10.1109/WACVW52041.2021.00015","url":null,"abstract":"Convolutional Neural Networks (CNNs) are being increasingly used to address the problem of iris presentation attack detection. In this work, we propose an explainable attention-guided iris presentation attack detector (AG-PAD) to augment CNNs with attention mechanisms and to provide visual explanations of model predictions. Two types of attention modules are independently placed on top of the last convolutional layer of the backbone network. Specifically, the channel attention module is used to model the inter-channel relationship between features, while the position attention module is used to model inter-spatial relationship between features. An element-wise sum is employed to fuse these two attention modules. Further, a novel hierarchical attention mechanism is introduced. Experiments involving both a JHU-APL proprietary dataset and the benchmark LivDet-Iris-2017 dataset suggest that the proposed method achieves promising detection results while explaining occurrences of salient regions for discriminative feature learning. To the best of our knowledge, this is the first work that exploits the use of attention mechanisms in iris presentation attack detection.","PeriodicalId":313062,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127119777","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: 20

Multi-Scale Voxel Class Balanced ASPP for LIDAR Pointcloud Semantic Segmentation 激光雷达点云语义分割的多尺度体素类平衡ASPP

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2021-01-01 DOI: 10.1109/WACVW52041.2021.00017

K. Kumar, S. Al-Stouhi

{"title":"Multi-Scale Voxel Class Balanced ASPP for LIDAR Pointcloud Semantic Segmentation","authors":"K. Kumar, S. Al-Stouhi","doi":"10.1109/WACVW52041.2021.00017","DOIUrl":"https://doi.org/10.1109/WACVW52041.2021.00017","url":null,"abstract":"This paper explores efficient techniques to improve PolarNet model performance to address the real-time semantic segmentation of LiDAR point clouds. The core framework consists of an encoder network, Atrous spatial pyramid pooling (ASPP)/Dense Atrous spatial pyramid pooling (DenseASPP) followed by a decoder network. Encoder extracts multi-scale voxel information in a top-down manner while decoder fuses multiple feature maps from various scales in a bottom-up manner. In between encoder and decoder block, an ASPP/DenseASPP block is inserted to enlarge receptive fields in a very dense manner. In contrast to PolarNet model, we use weighted cross entropy in conjunction with Lovasz-softmax loss to improve segmentation accuracy. Also this paper accelerates training mechanism of PolarNet model by incorporating learning-rate schedulers in conjunction with Adam optimizer for faster convergence with fewer epochs without degrading accuracy. Extensive experiments conducted on challenging SemanticKITTI dataset shows that our high-resolution-grid model obtains competitive state-of-art result of 60.6 mIOU @21fps whereas our low-resolution-grid model obtains 54.01 mIOU @35fps thereby balancing accuracy/speed trade-off.","PeriodicalId":313062,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129103521","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