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pro vyhledávání: '"Fu, Jiajun"'
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
Graph convolution networks (GCNs) have achieved remarkable performance in skeleton-based action recognition. However, previous GCN-based methods rely on elaborate human priors excessively and construct complex feature aggregation mechanisms, which li
Externí odkaz:
http://arxiv.org/abs/2308.16018
Autor:
Fu, Jiajun, Dang, Yonghao, Yin, Ruoqi, Zhang, Shaojie, Zhou, Feng, Zhao, Wending, Yin, Jianqin
This technical report describes our first-place solution to the pose estimation challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop. In this challenge, we aim to estimate human poses from in-the-wild stitched panora
Externí odkaz:
http://arxiv.org/abs/2303.07141
Human motion prediction is challenging due to the complex spatiotemporal feature modeling. Among all methods, graph convolution networks (GCNs) are extensively utilized because of their superiority in explicit connection modeling. Within a GCN, the g
Externí odkaz:
http://arxiv.org/abs/2204.01297
Autor:
Fu, Jiajun, Liu, Chao, Wang, Huixin, Song, Xinrong, Shi, Zhe, Guo, Xiaozhe, Li, Ziang, Wang, Qinghua
Publikováno v:
In Ceramics International 15 October 2024 50(20) Part B:39307-39317
Publikováno v:
In Nano Energy August 2024 127
Publikováno v:
In Ceramics International 1 July 2024 50(13) Part A:22733-22747
Publikováno v:
In Composites Part B 1 March 2024 272
Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task. Supervised methods such as Dual Domain Network (Du-DoNet) work well on simulation data; however, their performanc
Externí odkaz:
http://arxiv.org/abs/2103.04552
Autor:
Peng, Mingguo, Wang, Yicui, Wu, Chunge, Cai, Xuewen, Wu, Yao, Du, Erdeng, Zheng, Lu, Fu, Jiajun
Publikováno v:
In Biochemical and Biophysical Research Communications 26 November 2023 683