Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Chunlong Xia"'
Publikováno v:
Public Policy and Administration, Vol 22, Iss 4, Pp 418-433 (2024)
In response to the current situation in which an imbalance in educational redistribution has led to an inefficient and slow educational environment in the long-term, difficulty in satisfying the interests of subjects, and a lack of effective targetin
Externí odkaz:
https://doaj.org/article/97560e8efe1a43109f0c2ab303ecc1bf
Autor:
Qijia Liu1 liuqijialiulu@gmail.com, Chunlong Xia1 xiachunlong339@gmail.com, Ran Yi1 1330896399@qq.com, Ran Xu2 artynas@gmail.com, Yeong-Gil Kim3 ky3933@shinhan.ac
Publikováno v:
Public Policy & Administration / Viesoji Politika ir Administravimas. 2023, Vol. 22 Issue 4, p418-433. 16p.
Autor:
Qian, Li, Lin, Cai, Rubing, Wang, Chunlong, Xia, Guoqing, Cui, Cong, Li, Xuemei, Zheng, Xiyun, Cai
Publikováno v:
SSRN Electronic Journal.
A variety of semi-volatile banned pesticides (SVBPs) are ubiquitous in soils of mid-latitude regions. SVBPs undertake complicated soil-gas exchange processes in mid-latitude regions, challenging the understanding of source or sink roles of soils for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6ebea8298c57535cd0d9b8e680f8c73
https://doi.org/10.2139/ssrn.4148250
https://doi.org/10.2139/ssrn.4148250
Publikováno v:
2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS).
Non-rigid object tracking is an important yet challenging task in computer vision. In this paper, a multi-patch neural network (MPNet) model is presented to address the problem of non-rigid object tracking. The model learns a multiple patch based fra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5cfc50d8c6c96e3a71583f4fedba135
https://doi.org/10.1109/hpbdis.2019.8735493
https://doi.org/10.1109/hpbdis.2019.8735493
Autor:
Ping Wei, Chunlong Xia
Publikováno v:
2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP).
SIFT descriptor is one of the most widely-used local features for image matching. This paper presents an in-out region division method to improve the matching rate and the computational efficiency of SIFT descriptor. The descriptor region composed of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::efaf3ff43bd20bcc99abcfe5e864b5e7
https://doi.org/10.1109/siprocess.2017.8124514
https://doi.org/10.1109/siprocess.2017.8124514
Autor:
Yu, Bruce X.B.1 (AUTHOR) xinboyu@intl.zju.edu.cn, Chang, Jianlong2 (AUTHOR) jianlong.chang@huawei.com, Wang, Haixin3 (AUTHOR) wang.hx@stu.pku.edu.cn, Liu, Lingbo4 (AUTHOR) liulb@pcl.ac.cn, Wang, Shijie2 (AUTHOR) wangsj@mail.dlut.edu.cn, Wang, Zhiyu2 (AUTHOR) wangzhiyu10@huawei.com, Lin, Junfan4 (AUTHOR) linjf8@mail2.sysu.edu.cn, Xie, Lingxi2 (AUTHOR) 198808xc@gmail.com, Li, Haojie5 (AUTHOR) hjli@dlut.edu.cn, Lin, Zhouchen6 (AUTHOR) zlin@pku.edu.cn, Tian, Qi2 (AUTHOR) tian.qi1@huawei.com, Chen, Chang Wen7 (AUTHOR) changwen.chen@polyu.edu.hk
Publikováno v:
ACM Computing Surveys. Dec2024, Vol. 56 Issue 12, p1-38. 38p.
Publikováno v:
Julius-Kühn-Archiv; 2018, Issue 463, p328-337, 10p
Publikováno v:
Julius-Kühn-Archiv; 2018, Issue 463, p325-328, 4p