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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Why is the winner the best?

Why is the winner the best?

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M. D. Isensee, F. Adler, T. J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D. G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulme, A. Jannin, P. Kavur, A. E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martin-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Raedsch, T. Rafael-Patino, J. Bawa, V. Singh Speidel, S. Sudre, C. H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M. H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Bruengel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C. M. Fuller, C. Gaire, R. R. Galdran, A. Faura, A. Garcia Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martincic, T. Morais, P. Naser, M. A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Stepec, D. Subedi, R. Tochon, G. Torres, H. R. Urien, H. Vilaca, J. L. Wahid, K. A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jaeger, P. F. Kopp-Schneider, A. Maier-Hein, L. German Canc Res Ctr Div Intelligent Med Syst Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany German Canc Res Ctr Div Biostat Heidelberg Germany German Canc Res Ctr Div Med Image Comp Heidelberg Germany Univ Leeds Fac Engn & Phys Sci Sch Comp Leeds W Yorkshire England HES SO Valais Wallis Univ Appl Sci & Arts Western Inst Informat Sch Management Sierre Switzerland Lausanne Univ Hosp Dept Nucl Med & Mol Imaging Lausanne Switzerland Tech Hsch Ingolstadt Ingolstadt Germany Univ Penn Ctr Artificial Intelligence & Data Sci Integrated Philadelphia PA USA Univ Penn Ctr Biomed Image Comp & Analyt Philadelphia PA USA Univ Penn Dept Pathol & Lab Med Perelman Sch Med Philadelphia PA USA Univ Penn Dept Radiol Perelman Sch Med Philadelphia PA USA Univ Washington Dept Radiol Seattle WA USA UCL Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England UCL Dept Comp Sci London England Univ Autonoma Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain Natl Ctr Tumor Dis NCT UCC Div Translat Surg Oncol Dresden Germany Ist Italiano Tecnol Dept Adv Robot Genoa Italy Politecn Milan Dept Elect Informat & Bioengn Milan Italy IT Univ Copenhagen Copenhagen Denmark Heidelberg Univ Hosp Dept Gen Visceral & Transplantat Surg Heidelberg Germany Erasmus MC Dept Radiol & Nucl Med Biomed Imaging Grp Rotterdam Rotterdam Netherlands Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Appl Sci Western Switzerland HES SO Inst Informat Syst Sierre Switzerland Harvard Med Sch Brigham & Womens Hosp Boston MA USA Kings Coll London Sch Biomed Engn & Imaging Sci London England Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Essen Germany Univ Nebraska Med Ctr Omaha NE USA Heidelberg Univ Hosp Dept Internal Med 3 Heidelberg Germany Rigshosp Copenhagen Univ Hosp Neurobiol Res Unit Copenhagen Denmark Arab Acad Sci & Technol Cairo Egypt CIBM Ctr Biomed Imaging L

ISBN: (纸本)9798350301298

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of ieee ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. [GRAPHICS] .

关键词： cell microscopy Medical and biological vision

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NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Metho...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Abdelhamed, Abdelrahman Afifi, Mahmoud Timofte, Radu Brown, Michael S. Cao, Yue Zhang, Zhilu Zuo, Wangmeng Zhang, Xiaoling Liu, Jiye Chen, Wendong Wen, Changyuan Liu, Meng Lv, Shuailin Zhang, Yunchao Pan, Zhihong Li, Baopu Xi, Teng Fan, Yanwen Yu, Xiyu Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Yu, Songhyun Park, Bumjun Jeong, Jechang Liu, Shuai Zong, Ziyao Nan, Nan Li, Chenghua Yang, Zengli Bao, Long Wang, Shuangquan Bai, Dongwoon Lee, Jungwon Kim, Youngjung Rho, Kyeongha Shin, Changyeop Kim, Sungho Tang, Pengliang Zhao, Yiyun Zhou, Yuqian Fan, Yuchen Huang, Thomas Li, Zhihao Shah, Nisarg A. Liu, Wei Yan, Qiong Zhao, Yuzhi Mozejko, Marcin Latkowski, Tomasz Treszczotko, Lukasz Szafraniuk, Michal Trojanowski, Krzysztof Wu, Yanhong Michelini, Pablo Navarrete Hu, Fengshuo Lu, Yunhua Kim, Sujin Kim, Wonjin Lee, Jaayeon Choi, Jang-Hwan Zhussip, Magauiya Khassenov, Azamat Kim, Jong Hyun Cho, Hwechul Kansal, Priya Nathan, Sabari Ye, Zhangyu Lu, Xiwen Wu, Yaqi Yang, Jiangxin Cao, Yanlong Tang, Siliang Cao, Yanpeng Maggioni, Matteo Marras, Ioannis Tanay, Thomas Slabaugh, Gregory Yan, Youliang Kang, Myungjoo Choi, Han-Soo Song, Kyungmin Xu, Shusong Lu, Xiaomu Wang, Tingniao Lei, Chunxia Liu, Bin Gupta, Rajat Kumar, Vineet York Univ York N Yorkshire England Swiss Fed Inst Technol Zurich Switzerland Harbin Inst Technol Harbin Peoples R China Huawei Shenzhen Peoples R China Baidu Res Seattle WA USA Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Hanyang Univ Seoul South Korea North China Univ Technol Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Samsung Semicond Inc San Jose CA USA Agcy Def Dev Seoul South Korea Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Illinois Champaign IL USA Nanjing Univ Nanjing Peoples R China Indian Inst Technol Jodhpur Rajasthan India SenseTime Res Hong Kong Peoples R China TCL Res Europe Warsaw Poland BOE Artificial Intelligence & Big Data Res Inst Beijing Peoples R China Seoul Natl Univ Seoul South Korea ST Unitas Seoul South Korea Ewha Womans Univ Seoul South Korea Couger Inc Tokyo Japan Zhejiang Univ Hangzhou Peoples R China Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Harbin Inst Technol Shenzhen Shenzhen Peoples R China Huawei Technol Res & Dev UK Ltd Noahs Ark Lab London London England Dahua Technol Hangzhou Peoples R China Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9781728193601

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track similar to 250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://***/siddplus_data.

关键词： computer vision

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NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Eduardo Pé rez-Pellitero Sibi Catley-Chandar Richard Shaw Aleš Leonardis Radu Timofte Zexin Zhang Cen Liu Yunbo Peng Yue Lin Gaocheng Yu Jin Zhang Zhe Ma Hongbin Wang Xiangyu Chen Xintao Wang Haiwei Wu Lin Liu Chao Dong Jiantao Zhou Qingsen Yan Song Zhang Weiye Chen Yuhang Liu Zhen Zhang Yanning Zhang Javen Qinfeng Shi Dong Gong Dan Zhu Mengdi Sun Guannan Chen Yang Hu Haowei Li Baozhu Zou Zhen Liu Wenjie Lin Ting Jiang Chengzhi Jiang Xinpeng Li Mingyan Han Haoqiang Fan Jian Sun Shuaicheng Liu Juan Marí n-Vega Michael Sloth Peter Schneider-Kamp Richard Rö ttger Chunyang Li Long Bao Gang He Ziyao Xu Li Xu Gen Zhan Ming Sun Xing Wen Junlin Li Jinjing Li Chenghua Li Ruipeng Gang Fangya Li Chenming Liu Shuang Feng Fei Lei Rui Liu Junxiang Ruan Tianhong Dai Wei Li Zhan Lu Hengyan Liu Peian Huang Guangyu Ren Yonglin Luo Chang Liu Qiang Tu Sai Ma Yizhen Cao Steven Tel Barthelemy Heyrman Dominique Ginhac Chul Lee Gahyeon Kim Seonghyun Park An Gia Vien Truong Thanh Nhat Mai Howoon Yoon Tu Vo Alexander Holston Sheir Zaheer Chan Y. Park Huawei Noah&#x2019 s Ark Laboratory ETH Z&#x00FC rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under-or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

关键词： computer vision Runtime conferences Imaging Dynamic range Particle measurements Market research

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NTIRE 2021 Challenge on Video Super-Resolution

NTIRE 2021 Challenge on Video Super-Resolution

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sanghyun Son Suyoung Lee Seungjun Nah Radu Timofte Kyoung Mu Lee Kelvin C. K. Chan Shangchen Zhou Xiangyu Xu Chen Change Loy Boyuan Jiang Chuming Lin Yuchun Dong Donghao Luo Wenqing Chu Xiaozhong Ji Siqian Yang Ying Tai Chengjie Wang Jilin Li Feiyue Huang Chengpeng Chen Xiaojie Chu Jie Zhang Xin Lu Liangyu Chen Jing Lin Guodong Du Jia Hao Xueyi Zou Qi Zhang Lielin Jiang Xin Li He Zheng Fanglong Liu Dongliang He Fu Li Qingqing Dang Peng Yi Zhongyuan Wang Kui Jiang Junjun Jiang Jiayi Ma Yuxiang Chen Yutong Wang Ting Liu Qichao Sun Huanwei Liang Yiming Li Zekun Li Zhubo Ruan Fanjie Shang Chen Guo Haining Li Renjun Luo Longjie Shen Kassiani Zafirouli Konstantinos Karageorgos Konstantinos Konstantoudakis Anastasios Dimou Petros Daras Xiaowei Song Xu Zhuo Hanxi Liu Mengxi Guo Junlin Li Yu Li Ye Zhu Qing Wang Shijie Zhao Xiaopeng Sun Gen Zhan Tangxin Xie Yu Jia Yunhua Lu Wenhao Zhang Mengdi Sun Pablo Navarrete Michelini Xueheng Zhang Hao Jiang Zhiyu Chen Li Chen Zhiwei Xiong Zeyu Xiao Ruikang Xu Zhen Cheng Xueyang Fu Fenglong Song Zhipeng Luo Yuehan Yao Saikat Dutta Nisarg A. Shah Sourya Dipta Das Peng Zhao Yukai Shi Hongying Liu Fanhua Shang Yuanyuan Liu Fei Chen Fangxu Yu Ruisheng Gao Yixin Bai Jeonghwan Heo Shijie Yue Chenghua Li Jinjing Li Qian Zheng Ruipeng Gang Ruixia Song Seungwoo Wee Jechang Jeong Chen Li Geyingjie Wen Xinning Chai Li Song Seoul National University

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.

关键词： computer vision Casting conferences Superresolution Focusing pattern recognition Image restoration

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Simultaneous Identification and Tracking of Multiple People using Video and IMUs 32

Simultaneous Identification and Tracking of Multiple People ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Henschel, Roberto von Marcard, Timo Rosenhahn, Bodo Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9781728125060

Most modern approaches for multiple people tracking rely on human appearance to exploit similarity between person detections. In this work, we propose an alternative tracking method that does not depend on visual appearance and is still capable to deal with very dynamic motions and long-term occlusions. We make this feasible by: (i) incorporating additional information from body-worn inertial sensors, (ii) designing a neural network to relate person detections to orientation measurements and (iii) formulating a graph labeling problem to obtain a tracking solution that is globally consistent with the video and inertial recordings. We evaluate our approach on several challenging tracking sequences and achieve a very high IDF1 score of 91.2%. We outperform appearance-based baselines in scenarios where appearance is less informative and are on-par in situations with discriminative people appearance.

关键词： computer vision

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yawei Li Kai Zhang Radu Timofte Luc Van Gool Fangyuan Kong Mingxi Li Songwei Liu Zongcai Du Ding Liu Chenhui Zhou Jingyi Chen Qingrui Han Zheyuan Li Yingqi Liu Xiangyu Chen Haoming Cai Yu Qiao Chao Dong Long Sun Jinshan Pan Yi Zhu Zhikai Zong Xiaoxiao Liu Zheng Hui Tao Yang Peiran Ren Xuansong Xie Xian-Sheng Hua Yanbo Wang Xiaozhong Ji Chuming Lin Donghao Luo Ying Tai Chengjie Wang Zhizhong Zhang Yuan Xie Shen Cheng Ziwei Luo Lei Yu Zhihong Wen Qi Wul Youwei Li Haoqiang Fan Jian Sun Shuaicheng Liu Yuanfei Huang Meiguang Jin Hua Huang Jing Liu Xinjian Zhang Yan Wang Lingshun Long Gen Li Yuanfan Zhang Zuowei Cao Lei Sun Panaetov Alexander Yucong Wang Minjie Cai Li Wang Lu Tian Zheyuan Wang Hongbing Ma Jie Liu Chao Chen Yidong Cai Jie Tang Gangshan Wu Weiran Wang Shirui Huang Honglei Lu Huan Liu Keyan Wang Jun Chen Shi Chen Yuchun Miao Zimo Huang Lefei Zhang Mustafa Ayazoğ lu Wei Xiong Chengyi Xiong Fei Wang Hao Li Ruimian Wen Zhijing Yang Wenbin Zou Weixin Zheng Tian Ye Yuncheng Zhang Xiangzhen Kong Aditya Arora Syed Waqas Zamir Salman Khan Munawar Hayat Fahad Shahbaz Khan Dandan Gao Dengwen Zhou Qian Ning Jingzhu Tang Han Huang Yufei Wang Zhangheng Peng Haobo Li Wenxue Guan Shenghua Gong Xin Li Jun Liu Wanjun Wang Kun Zeng Hanjiang Lin Xinyu Chen Jinsheng Fang Computer Vision Lab ETH Zurich

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

关键词： Measurement Runtime Convolution conferences Superresolution Memory management Graphics processing units

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SIDOD: A Synthetic Image Dataset for 3D Object Pose recognition with Distractors 32

SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognit...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Jalal, Mona Spjut, Josef Boudaoud, Ben Betke, Margrit Boston Univ Boston MA 02215 USA NVIDIA Raleigh NC USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset [1]) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.

关键词： Three-dimensional displays Pose estimation Cameras Image segmentation computer vision Training Lighting

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Surrogate Contrastive Network for Supervised Band Selection in Multispectral computer vision Tasks 32

Surrogate Contrastive Network for Supervised Band Selection ...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bernal, Edgar A. Univ Rochester Rochester Data Sci Consortium 260 E Main St Rochester NY 14604 USA

ISBN: (纸本)9781728125060

computer vision techniques that operate on hyper- and multispectral imagery benefit from the additional amount of spectral information relative to those that exploit traditional RGB or monochromatic visual data. However, the increased volume of data to be processed brings about additional memory, storage and computational requirements. In order to address such limitations, a wide range of techniques for dimensionality reduction have been introduced by previous work. In this paper, we propose a framework for spectral band selection that is highly data- and computationally efficient. The method leverages a convolutional siamese network learned by optimizing a contrastive loss, and performs band selection based on the low-dimensional data embeddings produced by the network. We empirically demonstrate the efficacy of the method on an object detection task from aerial multispectral imagery. The results show that, in spite of the method's frugality, it produces very competitive band selection results against the evaluated competing techniques.

关键词： Object detection

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WiCV 2019: The Sixth Women In computer vision Workshop 32

WiCV 2019: The Sixth Women In Computer Vision Workshop

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Amerini, Irene Balashova, Elena Ebrahimi, Sayna Leonard, Kathryn Nagrani, Arsha Salvador, Amaia Univ Florence Florence Italy Princeton Univ Princeton NJ 08544 USA Univ Calif Berkeley Berkeley CA USA Occident Coll Los Angeles CA USA Univ Oxford Oxford England Univ Politen Catalunya Barcelona Spain

ISBN: (纸本)9781728125060

In this paper we present the Women in computer vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vision field. computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

关键词： computer vision

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Towards computer vision powered color-nutrient assessment of pureed food 32

Towards computer vision powered color-nutrient assessment of...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pfisterer, Kaylen J. Amelard, Robert Syrnyk, Braeden Wong, Alexander Univ Waterloo Vis & Image Proc Res Grp Waterloo ON Canada Schlegel UW Res Inst Aging Waterloo ON Canada

ISBN: (纸本)9781728125060

With one in four individuals afflicted with malnutrition, computer vision may provide a way of introducing a new level of automation in the nutrition field to reliably monitor food and nutrient intake. In this study, we present a novel approach to modeling the link between color and vitamin A content using transmittance imaging of a pureed foods dilution series in a computer vision powered nutrient sensing system via a fine-tuned deep autoencoder network, which in this case was trained to predict the relative concentration of sweet potato purees. Experimental results show the deep autoencoder network can achieve an accuracy of 80% across beginner (6 month) and intermediate (8 month) commercially prepared pureed sweet potato samples. Prediction errors may be explained by fundamental differences in optical properties which are further discussed.

关键词： Nutrients

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