Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Marras, Ioannis"'
Autor:
Babiloni, Francesca, Marras, Ioannis, Kokkinos, Filippos, Deng, Jiankang, Chrysos, Grigorios, Zafeiriou, Stefanos
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions. Such pairwise functions underpin the effectiveness of
Externí odkaz:
http://arxiv.org/abs/2107.02859
Autor:
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, Możejko, Marcin, Latkowski, Tomasz, Treszczotko, Lukasz, Szafraniuk, Michał, 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
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 w
Externí odkaz:
http://arxiv.org/abs/2005.04117
Autor:
Marras, Ioannis, Chrysos, Grigorios G., Alexiou, Ioannis, Slabaugh, Gregory, Zafeiriou, Stefanos
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising. Although the conditional image generation techniques have led to large improvements in this task, there has been little effo
Externí odkaz:
http://arxiv.org/abs/2002.04147
Autor:
Kokkinos, Filippos, Marras, Ioannis, Maggioni, Matteo, Slabaugh, Gregory, Zafeiriou, Stefanos
State-of-the-art methods for computer vision rely heavily on the translation equivariance and spatial sharing properties of convolutional layers without explicitly taking into consideration the input content. Modern techniques employ deep sophisticat
Externí odkaz:
http://arxiv.org/abs/1911.10581
Autor:
Marras, Ioannis
Computer vision, in general, aims to duplicate (or in some cases compensate) human vision, and traditionally, have been used in performing routine, repetitive tasks, such as classification in massive assembly lines. Today, research on computer vision
Externí odkaz:
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.695528
Akademický článek
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Publikováno v:
In Image and Vision Computing February 2017 58:3-12
Autor:
Babiloni, Francesca, Marras, Ioannis, Deng, Jiankang, Kokkinos, Filippos, Maggioni, Matteo, Chrysos, Grigorios, Torr, Philip, Zafeiriou, Stefanos
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence; November 2023, Vol. 45 Issue: 11 p12726-12737, 12p
Autor:
Digka Anna, Lyroudia Kleoniki, Kubinova Lucie, Karayannopoulou Georgia, Marras Ioannis, Pitas Ioannis
Publikováno v:
Balkan Journal of Dental Medicine, Vol 19, Iss 1, Pp 43-49 (2015)
The purpose of this study was the evaluation of 3 different histological methods for studying pulpal blood vessels in combination with 2 types of confocal microscope and computer assisted 3-dimensional reconstruction. 10 human, healthy, free of resto
Externí odkaz:
https://doaj.org/article/9684f0b96eb547829e63d0f3c250c1f7
Publikováno v:
In Image and Vision Computing October 2014 32(10):707-727