Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Kyeongha Rho"'
Publikováno v:
PLoS ONE, Vol 17, Iss 3 (2022)
In reinforcement learning, reward-driven feature learning directly from high-dimensional images faces two challenges: sample-efficiency for solving control tasks and generalization to unseen observations. In prior works, these issues have been addres
Externí odkaz:
https://doaj.org/article/bd57e6ce71354f308e95053807461aaf
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2efbd8779e7e016b63d2e9fcd770cd8e
https://doi.org/10.1109/cvpr52688.2022.00615
https://doi.org/10.1109/cvpr52688.2022.00615
Autor:
Boaz Arad, Radu Timofte, Ohad Ben-Shahar, Yi-Tun Lin, Graham Finlayson, Shai Givati, Jiaojiao Li, Chaoxiong Wu, Rui Song, Yunsong Li, Fei Liu, Zhiqiang Lang, Wei Wei, Lei Zhang, Jiangtao Nie, Yuzhi Zhao, Lai-Man Po, Qiong Yan, Wei Liu, Tingyu Lin, Youngjung Kim, Changyeop Shin, Kyeongha Rho, Sungho Kim, Zhiyu ZHU, Junhui HOU, He Sun, Jinchang Ren, Zhenyu Fang, Yijun Yan, Hao Peng, Xiaomei Chen, Jie Zhao, Tarek Stiebel, Simon Koppers, Dorit Merhof, Honey Gupta, Kaushik Mitra, Biebele Joslyn Fubara, Mohamed Sedky, Dave Dyke, Atmadeep Banerjee, Akash Palrecha, Sabarinathan sabarinathan, K Uma, D Synthiya Vinothini, B Sathya Bama, S M Md Mansoor Roomi
Publikováno v:
CVPR Workshops
This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole- scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e50ad478df948cdbebb174500d1606c8
https://strathprints.strath.ac.uk/77252/1/Arad_etal_IEEE_2020_NTIRE_2020_challenge_on_spectral_reconstruction_from_an_RGB.pdf
https://strathprints.strath.ac.uk/77252/1/Arad_etal_IEEE_2020_NTIRE_2020_challenge_on_spectral_reconstruction_from_an_RGB.pdf
Autor:
Kyeongha Rho, Qiong Yan, Marcin Mozejko, Jong Hyun Kim, Abdelrahman Abdelhamed, Kyungmin Song, Ioannis Marras, Youliang Yan, Matteo Maggioni, Yunhua Lu, Jiye Liu, Songhyun Yu, Krzysztof Trojanowski, Gregory G. Slabaugh, Pengliang Tang, Wei Liu, Xiaoling Zhang, Han Junyu, Jaayeon Lee, Gang Zhang, Sungho Kim, Yanhong Wu, Yuzhi Zhao, Zhangyu Ye, Tingniao Wang, Wonjin Kim, Yaqi Wu, Bumjun Park, Shusong Xu, Lukasz Treszczotko, Yunchao Zhang, Xiaomu Lu, Jingtuo Liu, Yanwen Fan, Zengli Yang, Yue Cao, Thomas Tanay, Xiyu Yu, Wangmeng Zuo, Tomasz Latkowski, Teng Xi, Sabari Nathan, Chenghua Li, Siliang Tang, Sujin Kim, Magauiya Zhussip, Xiwen Lu, Changyeop Shin, Fengshuo Hu, Yanpeng Cao, Michal Szafraniuk, Jechang Jeong, Jiangxin Yang, Mahmoud Afifi, Baopu Li, Ziyao Zong, Shuangquan Wang, Zhilu Zhang, Bin Liu, Jungwon Lee, Nan Nan, Youngjung Kim, Zhihao Li, Rajat Gupta, Shuailin Lv, Nisarg A. Shah, Hwechul Cho, Radu Timofte, Changyuan Wen, Yanlong Cao, Thomas S. Huang, Azamat Khassenov, Wendong Chen, Myungjoo Kang, Long Bao, Yuchen Fan, Dongwoon Bai, Yuqian Zhou, Jang-Hwan Choi, Pablo Navarrete Michelini, Meng Liu, Yiyun Zhao, Vineet Kumar, Michael S. Brown, Chunxia Lei, Zhihong Pan, Han-Soo Choi, Shuai Liu, Errui Ding, Priya Kansal
Publikováno v:
CVPR Workshops
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51816e148a367ae0db3741e02bd2b8bd
https://doi.org/10.1109/cvprw50498.2020.00256
https://doi.org/10.1109/cvprw50498.2020.00256
Autor:
Dongyeop Kang, Joseph P. Park, Minjae Do, Kyeongha Rho, Haeshin Lee, Sang Hyeon Hong, Jung Hoon Ahn, Jaewook Ryu
Publikováno v:
Advanced Materials Interfaces. 6:1900379
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9217eac2b14210a141cb7a791b003a14
https://doi.org/10.1002/admi.201900379
https://doi.org/10.1002/admi.201900379