Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Zhang, Yiman"'
Autor:
Shu, Han, Li, Wenshuo, Tang, Yehui, Zhang, Yiman, Chen, Yihao, Li, Houqiang, Wang, Yunhe, Chen, Xinghao
Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed various applications based on the pretrained SAM and achieved impressive
Externí odkaz:
http://arxiv.org/abs/2312.13789
Autor:
Ding, Kai, Chen, Liyao, Zhang, Yuting, Ge, Siyu, Zhang, Yiman, Lu, Meng, Shen, Zhenming, Tong, Zaikang, Zhang, Junhong
Publikováno v:
In Catena September 2024 244
Ion transport in composites of binary electrolyte and single ion conductor—A chronoamperometry study
Publikováno v:
In Electrochimica Acta 1 August 2024 494
Publikováno v:
In Food Chemistry 1 November 2024 457
Autor:
Ding, Kai, Zhang, Yuting, Ge, Siyu, Zhang, Yiman, Lu, Meng, Shen, Zhenming, Tong, Zaikang, Zhang, Junhong
Publikováno v:
In European Journal of Agronomy July 2024 157
Autor:
Wang, Zongwei, Guan, Lina, Zhang, Yiman, Hou, Xinyu, Wang, Ziyuan, Gao, Fei, Ye, Gaoqi, Wang, Jing, Liu, Jie
Publikováno v:
In Journal of Functional Foods June 2024 117
Autor:
Wei, Yulong, Zhang, Yiman, Wang, Ziyuan, Yang, Zihui, Wang, Zongwei, Hao, Yiming, Li, Genying, Gao, Fei, Ye, Gaoqi, Wang, Jing, Liu, Jie
Publikováno v:
In International Journal of Biological Macromolecules June 2024 270 Part 1
Autor:
Liu, Jie, Zhang, Yiman, Liu, Jiayuan, Zhang, Huijuan, Gong, Lingxiao, Li, Zhaofeng, Liu, Hongzhi, Wang, Ziyuan
Publikováno v:
In Food Hydrocolloids April 2024 149
Autor:
Han, Kai, Wang, Yunhe, Chen, Hanting, Chen, Xinghao, Guo, Jianyuan, Liu, Zhenhua, Tang, Yehui, Xiao, An, Xu, Chunjing, Xu, Yixing, Yang, Zhaohui, Zhang, Yiman, Tao, Dacheng
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities, researchers are looking at ways to apply transfor
Externí odkaz:
http://arxiv.org/abs/2012.12556
This paper focuses on channel pruning for semantic segmentation networks. Previous methods to compress and accelerate deep neural networks in the classification task cannot be straightforwardly applied to the semantic segmentation network that involv
Externí odkaz:
http://arxiv.org/abs/2007.08386