Zobrazeno 1 - 10
of 794
pro vyhledávání: '"Wang Luping"'
Autor:
Sun Lu, Zhang Canwei, Song Yuwen, Li Jianxin, Duan Lian, Gao Yang, Xie Yuemei, Wang Luping, Dang Guangfu
Publikováno v:
Guoji Yanke Zazhi, Vol 24, Iss 5, Pp 677-685 (2024)
AIM:To identify transcriptional differences between the ocular surface ectoderm(OSE)and surface ectoderm(SE)using RNA-seq, and elucidate the OSE transcriptome landscape and the regulatory networks involved in its development.METHODS:OSE and SE cells
Externí odkaz:
https://doaj.org/article/7a7220b0a443464a99e3bddf521fc44d
Publikováno v:
Nantong Daxue xuebao. Ziran kexue ban, Vol 20, Iss 1, Pp 14-27 (2021)
Metal organic frameworks(MOFs) is a new type of crystalline porous material, which has the characteristics of high surface area, high porosity and low synthesis cost. MOFs have been widely used in the field of catalysis due to its unique porous struc
Externí odkaz:
https://doaj.org/article/0ba45939d4754c5db9fa067a439e68ea
Autor:
Zhang, Howard, Ba, Yunhao, Yang, Ethan, Upadhyay, Rishi, Wong, Alex, Kadambi, Achuta, Guo, Yun, Xiao, Xueyao, Wang, Xiaoxiong, Li, Yi, Chang, Yi, Yan, Luxin, Zheng, Chaochao, Wang, Luping, Liu, Bin, Khowaja, Sunder Ali, Yoon, Jiseok, Lee, Ik-Hyun, Zhang, Zhao, Wei, Yanyan, Ren, Jiahuan, Zhao, Suiyi, Zheng, Huan
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023. The aim of this competition is to study the rainy weather phenomenon in real world scenarios, provide a novel real world rainy image
Externí odkaz:
http://arxiv.org/abs/2403.12327
Autor:
Cioppa, Anthony, Giancola, Silvio, Somers, Vladimir, Magera, Floriane, Zhou, Xin, Mkhallati, Hassan, Deliège, Adrien, Held, Jan, Hinojosa, Carlos, Mansourian, Amir M., Miralles, Pierre, Barnich, Olivier, De Vleeschouwer, Christophe, Alahi, Alexandre, Ghanem, Bernard, Van Droogenbroeck, Marc, Kamal, Abdullah, Maglo, Adrien, Clapés, Albert, Abdelaziz, Amr, Xarles, Artur, Orcesi, Astrid, Scott, Atom, Liu, Bin, Lim, Byoungkwon, Chen, Chen, Deuser, Fabian, Yan, Feng, Yu, Fufu, Shitrit, Gal, Wang, Guanshuo, Choi, Gyusik, Kim, Hankyul, Guo, Hao, Fahrudin, Hasby, Koguchi, Hidenari, Ardö, Håkan, Salah, Ibrahim, Yerushalmy, Ido, Muhammad, Iftikar, Uchida, Ikuma, Be'ery, Ishay, Rabarisoa, Jaonary, Lee, Jeongae, Fu, Jiajun, Yin, Jianqin, Xu, Jinghang, Nang, Jongho, Denize, Julien, Li, Junjie, Zhang, Junpei, Kim, Juntae, Synowiec, Kamil, Kobayashi, Kenji, Zhang, Kexin, Habel, Konrad, Nakajima, Kota, Jiao, Licheng, Ma, Lin, Wang, Lizhi, Wang, Luping, Li, Menglong, Zhou, Mengying, Nasr, Mohamed, Abdelwahed, Mohamed, Liashuha, Mykola, Falaleev, Nikolay, Oswald, Norbert, Jia, Qiong, Pham, Quoc-Cuong, Song, Ran, Hérault, Romain, Peng, Rui, Chen, Ruilong, Liu, Ruixuan, Baikulov, Ruslan, Fukushima, Ryuto, Escalera, Sergio, Lee, Seungcheon, Chen, Shimin, Ding, Shouhong, Someya, Taiga, Moeslund, Thomas B., Li, Tianjiao, Shen, Wei, Zhang, Wei, Li, Wei, Dai, Wei, Luo, Weixin, Zhao, Wending, Zhang, Wenjie, Yang, Xinquan, Ma, Yanbiao, Joo, Yeeun, Zeng, Yingsen, Gan, Yiyang, Zhu, Yongqiang, Zhong, Yujie, Ruan, Zheng, Li, Zhiheng, Huang, Zhijian, Meng, Ziyu
The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadc
Externí odkaz:
http://arxiv.org/abs/2309.06006
This technical report presents our solution to Ball Action Spotting in videos. Our method reached second place in the CVPR'23 SoccerNet Challenge. Details of this challenge can be found at https://www.soccer-net.org/tasks/ball-action-spotting. Our ap
Externí odkaz:
http://arxiv.org/abs/2306.05772
This technical report presents our Restormer-Plus approach, which was submitted to the GT-RAIN Challenge (CVPR 2023 UG$^2$+ Track 3). Details regarding the challenge are available at http://cvpr2023.ug2challenge.org/track3.html. Restormer-Plus outper
Externí odkaz:
http://arxiv.org/abs/2305.05454
Autor:
Wang, Luping, Liu, Bin
Detection Transformer (DETR) is a Transformer architecture based object detection model. In this paper, we demonstrate that it can also be used as a data augmenter. We term our approach as DETR assisted CutMix, or DeMix for short. DeMix builds on Cut
Externí odkaz:
http://arxiv.org/abs/2304.04554
Autor:
Zhang, Gege1 (AUTHOR) zhanggg3@mail2.sysu.edu.cn, Wang, Luping1 (AUTHOR), Chen, Zengping1 (AUTHOR) chenzengp@mail.sysu.edu.cn
Publikováno v:
Remote Sensing. Aug2024, Vol. 16 Issue 15, p2722. 20p.
Publikováno v:
In Displays September 2024 84
Autor:
Wu, Shiyao, Qiu, Liewei, Wang, Luping, He, Yani, Wang, Qian, Liu, Hongchen, Shi, Kunmou, Tian, Yuanyu, Tang, Ruiyuan, Che, Yuanjun
Publikováno v:
In Fuel 15 July 2024 368