2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)最新文献_第10页

Advanced 3D Deep Non-Local Embedded System for Self-Augmented X-Ray-based COVID-19 Assessment 基于自增强x射线的新型冠状病毒肺炎评估先进3D深度非局部嵌入式系统

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00051

F. Rundo, A. Genovese, R. Leotta, F. Scotti, V. Piuri, S. Battiato

{"title":"Advanced 3D Deep Non-Local Embedded System for Self-Augmented X-Ray-based COVID-19 Assessment","authors":"F. Rundo, A. Genovese, R. Leotta, F. Scotti, V. Piuri, S. Battiato","doi":"10.1109/ICCVW54120.2021.00051","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00051","url":null,"abstract":"COVID-19 diagnosis using chest x-ray (CXR) imaging has a greater sensitivity and faster acquisition procedures than the Real-Time Polimerase Chain Reaction (RT-PCR) test, also requiring radiology machinery that is cheap and widely available. To process the CXR images, methods based on Deep Learning (DL) are being increasingly used, often in combination with data augmentation techniques. However, no method in the literature performs data augmentation in which the augmented training samples are processed collectively as a multi-channel image. Furthermore, no approach has yet considered a combination of attention-based networks with Convolutional Neural Networks (CNN) for COVID-19 detection. In this paper, we propose the first method for COVID-19 detection from CXR images that uses an innovative self-augmentation scheme based on reinforcement learning, which combines all the augmented images in a 3D deep volume and processes them together using a novel non-local deep CNN, which integrates convolutional and attention layers based on non-local blocks. Results on publicly-available databases exhibit a greater accuracy than the state of the art, also showing that the regions of CXR images influencing the decision are consistent with radiologists’ observations.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"15 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":"127366116","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: 5

Learning Laplacians in Chebyshev Graph Convolutional Networks 学习切比雪夫图卷积网络中的拉普拉斯算子

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00234

H. Sahbi

{"title":"Learning Laplacians in Chebyshev Graph Convolutional Networks","authors":"H. Sahbi","doi":"10.1109/ICCVW54120.2021.00234","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00234","url":null,"abstract":"Spectral graph convolutional networks (GCNs) are particular deep models which aim at extending neural networks to arbitrary irregular domains. The principle of these networks consists in projecting graph signals using the eigen-decomposition of their Laplacians, then achieving filtering in the spectral domain prior to back-project the resulting filtered signals onto the input graph domain. However, the success of these operations is highly dependent on the relevance of the used Laplacians which are mostly handcrafted and this makes GCNs clearly sub-optimal. In this paper, we introduce a novel spectral GCN that learns not only the usual convolutional parameters but also the Laplacian operators. The latter are designed \"end-to-end\" as a part of a recursive Chebyshev decomposition with the particularity of conveying both the differential and the non-differential properties of the learned representations – with increasing order and discrimination power – without overparametrizing the trained GCNs. Extensive experiments, conducted on the challenging task of skeleton-based action recognition, show the generalization ability and the outperformance of our proposed Laplacian design w.r.t. different baselines (built upon handcrafted and other learned Laplacians) as well as the related work.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"1 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":"126663962","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-Input Fusion for Practical Pedestrian Intention Prediction 实用行人意图预测的多输入融合

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00260

Ankur Singh, U. Suddamalla

{"title":"Multi-Input Fusion for Practical Pedestrian Intention Prediction","authors":"Ankur Singh, U. Suddamalla","doi":"10.1109/ICCVW54120.2021.00260","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00260","url":null,"abstract":"Pedestrians are the most vulnerable road users and are at a high risk of fatal accidents. Accurate pedestrian detection and effectively analyzing their intentions to cross the road are critical for autonomous vehicles and ADAS solutions to safely navigate public roads. Faster and precise estimation of pedestrian intention helps in adopting safe driving behavior. Visual pose and motion are two important cues that have been previously employed to determine pedestrian intention. However, motion patterns can give erroneous results for short-term video sequences and are thus prone to mistakes. In this work, we propose an intention prediction network that utilizes pedestrian bounding boxes, pose, bounding box coordinates, and takes advantage of global context along with the local setting. This network implicitly learns pedestrians’ motion cues and location information to differentiate between a crossing and a non-crossing pedestrian. We experiment with different combinations of input features and propose multiple efficient models in terms of accuracy and inference speeds. Our best-performing model shows around 85% accuracy on the JAAD dataset.","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":"127493258","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: 10

Adaptive Distribution Learning with Statistical Hypothesis Testing for COVID-19 CT Scan Classification 基于统计假设检验的自适应分布学习新冠肺炎CT扫描分类

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00057

Guan-Lin Chen, Chih-Chung Hsu, Mei-Hsuan Wu

{"title":"Adaptive Distribution Learning with Statistical Hypothesis Testing for COVID-19 CT Scan Classification","authors":"Guan-Lin Chen, Chih-Chung Hsu, Mei-Hsuan Wu","doi":"10.1109/ICCVW54120.2021.00057","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00057","url":null,"abstract":"With the massive damage in the world caused by Coronavirus Disease 2019 SARS-CoV-2 (COVID-19), many related research topics have been proposed in the past two years. The Chest Computed Tomography (CT) scan is the most valuable materials to diagnose the COVID-19 symptoms. However, most schemes for COVID-19 classification of Chest CT scan are based on single slice-level schemes, implying that the most critical CT slice should be selected from the original CT volume manually. In this paper, a statistical hypothesis test is adopted to the deep neural network to learn the implicit representation of CT slices. Specifically, we propose an Adaptive Distribution Learning with Statistical hypothesis Testing (ADLeaST) for COVID-19 CT scan classification can be used to judge the importance of each slice in CT scan and followed by adopting the non-parametric statistics method, Wilcoxon signed-rank test, to make predicted result explainable and stable. In this way, the impact of out-of-distribution (OOD) samples can be significantly reduced. Meanwhile, a self-attention mechanism without statistical analysis is also introduced into the back-bone network to learn the importance of the slices explicitly. The extensive experiments show that both the proposed schemes are stable and superior. Our experiments also demonstrated that the proposed ADLeaST significantly outperforms the state-of-the-art methods.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"26 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":"124901570","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: 6

Machine learning meets distinctness in variety testing 机器学习满足多样性测试的独特性

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00151

Geoffroy Couasnet, Mouad Zine El Abidine, F. Laurens, H. Dutagaci, D. Rousseau

{"title":"Machine learning meets distinctness in variety testing","authors":"Geoffroy Couasnet, Mouad Zine El Abidine, F. Laurens, H. Dutagaci, D. Rousseau","doi":"10.1109/ICCVW54120.2021.00151","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00151","url":null,"abstract":"Distinctness is a binary trait used in variety testing to determine if a new plant variety can be considered distinct or not from a set of already existing varieties. Currently distinctness is mostly based on human visual perception. This communication considers distinctness with a machine learning perspective where distinctness is evaluated through an identification process based on information extraction from machine vision. Illustrations are provided on apple variety testing to perform distinctness based on color. An automated pipeline of image acquisition, processing and supervised learning is proposed. A feature space based on the 3D color histogram of a set of apples is built. This feature space is built using optimal transport, fractal dimension, mutual entropy and fractional anisotropy and it provides results in accordance with human expertise when applied to a set of varieties highly contrasted in color and another one with low color contrast. These results open new research directions for achieving higher-throughput, higher reproducibility and higher statistical confidence in variety testing.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"42 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":"125088996","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

ICCV 2021 Organizers

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/iccvw54120.2021.00006

引用次数: 0

CryoPoseNet: End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data CryoPoseNet:端到端同步学习单粒子取向和三维地图重建从低温电子显微镜数据

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00452

Y. Nashed, F. Poitevin, Harshit Gupta, G. Woollard, M. Kagan, C. Yoon, D. Ratner

引用次数: 13

Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking 用于高性能视觉跟踪的学习空间-外观记忆网络

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00302

Fei Xie, Wankou Yang, Kaihua Zhang, Bo Liu, Guangting Wang, W. Zuo

{"title":"Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking","authors":"Fei Xie, Wankou Yang, Kaihua Zhang, Bo Liu, Guangting Wang, W. Zuo","doi":"10.1109/ICCVW54120.2021.00302","DOIUrl":"https://doi.org/10.1109/ICCVW54120.2021.00302","url":null,"abstract":"Segmentation-based tracking is currently a promising tracking paradigm due to the robustness towards non-grid deformations, comparing to the traditional box-based tracking methods. However, existing segmentation-based trackers are insufficient in modeling and exploiting dense pixel-wise correspondence across frames. To overcome these limitations, this paper presents a novel segmentation-based tracking architecture equipped with spatio-appearance memory networks. The appearance memory network utilizes spatio-temporal non-local similarity to propagate segmentation mask to the current frame, which can effectively capture long-range appearance variations and we further treat discriminative correlation filter as spatial memory bank to store the mapping between feature map and spatial map. Moreover, mutual promotion on dual memory networks greatly boost the overall tracking performance. We further propose a dynamic memory machine (DMM) which employs the Earth Mover’s Distance (EMD) to reweight memory samples. Without bells and whistles, our simple-yet-effective tracking architecture sets a new state-of-the-art on six tracking benchmarks. Besides, our approach achieves comparable results on two video object segmentation benchmarks. Code and model are released at https://github.com/phiphiphi31/DMB.","PeriodicalId":226794,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","volume":"176 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":"121541197","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: 7

Contrastive Feature Loss for Image Prediction 图像预测中的对比特征损失

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00220

A. Andonian, Taesung Park, Bryan C. Russell, Phillip Isola, Jun-Yan Zhu, Richard Zhang

引用次数: 11

GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021 GIAOTracker:具有全球信息和优化策略的综合MCMOT框架

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2021-10-01 DOI: 10.1109/ICCVW54120.2021.00315

Yunhao Du, Jun-Jun Wan, Yanyun Zhao, Binyu Zhang, Zhihang Tong, Junhao Dong

引用次数: 36