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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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WiCV 2018: The Fourth Women In computer vision Workshop 31

WiCV 2018: The Fourth Women In Computer Vision Workshop

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Demir, Ilke Bazazian, Dena Romero, Adriana Sharmanska, Viktoriia Tchapmi, Lyne P. Facebook Menlo Pk CA 94025 USA Comp Vis Ctr Barcelona Spain Facebook AI Res Montreal PQ Canada Imperial Coll London London England Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present WiCV 2018 - Women in computer vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with cvpr 2018. computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to female-identifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.

关键词： conferences computer vision Industries Object recognition Engineering profession Collaboration Machine learning

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NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results 32

NTIRE 2019 Challenge on Real Image Super-Resolution: Methods...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cai, Jianrui Gu, Shuhang Timofte, Radu Zhang, Lei Liu, Xiao Ding, Yukang He, Dongliang Li, Chao Fu, Yi Wen, Shilei Feng, Ruicheng Gu, Jinjin Qiao, Yu Dong, Chao Park, Dongwon Chun, Se Young Yoon, Sanghoon Kwak, Junhyung Son, Donghee Zamir, Syed Waqas Arora, Aditya Khan, Salman Khan, Fahad Shahbaz Shao, Ling Wei, Zhengping Liu, Lei Cai, Hong Li, Darui Gao, Fujie Hui, Zheng Wang, Xiumei Gao, Xinbo Cheng, Guoan Matsune, Ai Li, Qiuyu Zhu, Leilei Zang, Huaijuan Zhan, Shu Qiu, Yajun Wang, Ruxin Li, Jiawei Jing, Yongcheng Song, Mingli Liu, Pengju Zhang, Kai Liu, Jingdong Liu, Jiye Zhang, Hongzhi Zuo, Wangmeng Tang, Wenyi Liu, Jing Kim, Youngjung Shin, Changyeop Kim, Minbeom Kim, Sungho Michelini, Pablo Navarrete Liu, Hanwen Zhu, Dan Xu, Xuan Li, Xin Bai, Furui Sun, Xiaopeng Zha, Lin Huang, Yuanfei Lu, Wen Cao, Yanpeng Chen, Du He, Zewei Sun Anshun Tang, Siliang Fan, Hongfei Li, Xiang Li, Guo Zhang, Wenjie Zhang, Yumei He, Qingwen Qin, Jinghui Huang, Lishan Shi, Yukai Wei, Pengxu Wen, Wushao Lin, Liang Yu, Jun Xie, Guochen Li, Mengyan Chen, Rong Luo, Xiaotong Hong, Chen Qu, Yanyun Li, Cuihua Liu, Zhi-Song Wang, Li-Wen Li, Chu-Tak Zhao, Can Li, Bowen Tsai, Chung-Chi Chuang, Shang-Chih Choi, Joon-Hee Kim, Joonsoo Jiang, Xiaoyun Pan, Ze Lv, Qunbo Tan, Zheng He, Peidong Hong Kong Polytech Univ Hong Kong Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Baidu Inc Comp Vis Technol VIS Dept Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Beijing Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Ulsan Natl Inst Sci & Technol UNIST Ulsan South Korea Kakao Jeju Si South Korea CASTIS Multimedia R&D Ctr Seoul South Korea NALBI Inc Seoul South Korea Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates LiveMe Inc West Hollywood CA USA Xidian Univ Sch Elect Engn Xian Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Yunnan Univ Kunming Yunnan Peoples R China Alibaba Grp Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Harbin Inst Technol Sch Comp Sci & Technol 92 West Da Zhi St Harbin Peoples R China Harbin Ultravis Technol 6 Fuhua St Harbin Peoples R China East China Normal Univ ECNU Multimedia & Comp Vis Lab Shanghai Peoples R China Agcy Def Dev Seoul South Korea BOE Technol Grp Co Ltd Beijing Peoples R China West Virginia Univ Morgantown WV 26506 USA Xidian Univ Xian Peoples R China Kiwi Image Technol Co Ltd Xian Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Zhejiang Peoples R China Beijing Kingsoft Cloud Technol Co Ltd Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Univ Sci & Technol China Beijing Peoples R China XiDian Univ Kiwi Image Technol Co Ltd Xian Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA Qualcomm Technol Inc San Diego CA USA

ISBN: (纸本)9781728125060

This paper reviewed the 3rd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with a focus on proposed solutions and results. The challenge had 1 track, which was aimed at the real-world single image super-resolution problem with an unknown scaling factor. Participants were mapping low-resolution images captured by a DSLR camera with a shorter focal length to their high-resolution images captured at a longer focal length. With this challenge, we introduced a novel real-world super-resolution dataset (RealSR). The track had 403 registered participants, and 36 teams competed in the final testing phase. They gauge the state-of-the-art in real-world single image super-resolution.

关键词： computer vision

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On Visible Adversarial Perturbations & Digital Watermarking 31

On Visible Adversarial Perturbations & Digital Watermarking

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hayes, Jamie UCL London England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Given a machine learning model, adversarial perturbations transform images such that the model's output is classified as an attacker chosen class. Most research in this area has focused on adversarial perturbations that are imperceptible to the human eye. However, recent work has considered attacks that are perceptible but localized to a small region of the image. Under this threat model, we discuss both defenses that remove such adversarial perturbations, and attacks that can bypass these defenses.

关键词： Perturbation methods Watermarking Computational modeling Visualization Task analysis Image restoration computer vision

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Disguised Faces in the Wild 31

Disguised Faces in the Wild

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kushwaha, Vineet Singh, Maneet Singh, Richa Vatsa, Mayank Ratha, Nalini Chellappa, Rama IIIT Delhi Delhi India IBM TJ Watson Res Ctr Ossining NY USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781538661000

Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of cvpr2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition.

关键词： computer vision pattern recognition conferences

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Fine-grained Activity recognition in Baseball Videos 31

Fine-grained Activity Recognition in Baseball Videos

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Piergiovanni, A. J. Ryoo, Michael S. Indiana Univ Dept Comp Sci Bloomington IN 47408 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos. We experimentally compare various recognition approaches capturing temporal structure in activity videos, by classifying segmented videos and extending those approaches to continuous videos. We also compare models on the extremely difficult task of predicting pitch speed and pitch type from broadcast baseball videos. We find that learning temporal structure is valuable for fine-grained activity recognition.

关键词： Videos Activity recognition Sports Task analysis Cameras computer vision Games

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Land Cover Classification from Satellite Imagery With U-Net and Lovasz-Softmax Loss 31

Land Cover Classification from Satellite Imagery With U-Net ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rakhlin, Alexander Davydow, Alex Nikolenko, Sergey Neuromat OU EE-10111 Tallinn Estonia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The land cover classification task of the DeepGlohe Challenge presents significant obstacles even to state of the art segmentation models due to a small amount of data, incomplete and sometimes incorrect labeling, and highly imbalanced classes. In this work, we show an approach based on the U-Net architecture with the Lovcisz-Softmax loss that successfully alleviates these problems: we compare several different convolutional architectures for U-Net encoders.

关键词： Image segmentation Satellites computer architecture Training Task analysis computer vision Stochastic processes

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Fusing Visual Saliency for Material recognition 31

Fusing Visual Saliency for Material Recognition

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Qi, Lin Xu, Ying Shang, Xiaowei Dong, Junyu Ocean Univ China 238 Songling Rd Qingdao 266100 Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Material recognition is researched in both computer vision and vision science fields. In this paper, we investigated how humans observe material images and found the eye fixation information improves the performance of material image classification models. We first collected eye-tracking data from human observers and used it to fine-tune a generative adversarial network for saliency prediction (SalGAN). We then fused the predicted saliency map with material images and fed them to CNN models for material classification. The experiment results show that the classification accuracy is improved than those using original images. This indicates that human's visual cues could benefit computational models as priors.

关键词： Computational modeling computer vision Visualization Data models Task analysis Training pattern recognition

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Road detection with EOSResUNet and post vectorizing algorithm 31

Road detection with EOSResUNet and post vectorizing algorith...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Filin, Oleksandr Zapara, Anton Panchenko, Serhii EOS Data Analyt Menlo Pk CA 94027 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition. This was facilitated by many factors, such as a high number of satellites with high-resolution imagery, the significant development of computer vision, especially with a major breakthrough in the field of convolutional neural networks, a wide range of industry verticals for usage and still a quite empty market. Roads are one of the most popular objects for recognition. In this article, we want to present you the combination of work of neural network and postprocessing algorithm, due to which we get not only the coverage mask but also the vectors of all of the individual roads that are present in the image and can be used to address the higher-level tasks in the future. This approach was used to solve the DeepGlobe Road Extraction Challenge.

关键词： Roads Neural networks Task analysis Satellites computer vision Earth Observing System Data analysis

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Stacked U-Nets with Multi-Output for Road Extraction 31

Stacked U-Nets with Multi-Output for Road Extraction

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sun, Tao Chen, Zehui Yang, Wenxiang Wang, Yin Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

With the recent advances of Convolutional Neural Networks (CNN) in computer vision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In this paper, we propose a new method for road extraction using stacked U-Nets with multiple output. A hybrid loss function is used to address the problem of unbalanced classes of training data. Post-processing methods, including road map vectorization and shortest path search with hierarchical thresholds, help improve recall. The overall improvement of mean IoU compared to the vanilla VGG network is more than 20%.

关键词： Roads Decoding computer vision Encoding Feature extraction Neural networks Satellites

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A Compact Deep Learning Model for Robust Facial Expression recognition 31

A Compact Deep Learning Model for Robust Facial Expression R...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kuo, Chieh-Ming Lai, Shang-Hong Sarkis, Michel Natl Tsing Hua Univ Hsinchu Taiwan Qualcomm Technol Inc San Diego CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose a compact frame-based facial expression recognition framework for facial expression recognition which achieves very competitive performance with respect to state-of-the-art methods while using much less parameters. The proposed framework is extended to a frame-to-sequence approach by exploiting temporal information with gated recurrent units. In addition, we develop an illumination augmentation scheme to alleviate the over-fitting problem when training the deep networks with hybrid data sources. Finally, we demonstrate the performance improvement by using the proposed technique on some public datasets.

关键词： Databases Training Face recognition Hidden Markov models Face Image recognition Image sequences

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