no code implementations • 16 Jul 2020 • Yunxiao Qin, Wei-Guo Zhang, Zezheng Wang, Chenxu Zhao, Jingping Shi
LWAU is inspired by an interesting finding that compared with common deep models, the meta-learner pays much more attention to update its top layer when learning from few images.
no code implementations • 29 Apr 2019 • Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu, Zezheng Wang, Zitong Yu, Tianyu Fu, Feng Zhou, Jingping Shi, Zhen Lei
Therefore, we define face anti-spoofing as a zero- and few-shot learning problem.
no code implementations • 11 Dec 2018 • Yunxiao Qin, WeiGuo Zhang, Chenxu Zhao, Zezheng Wang, Xiangyu Zhu, Guo-Jun Qi, Jingping Shi, Zhen Lei
In this paper, inspired by the human cognition process which utilizes both prior-knowledge and vision attention in learning new knowledge, we present a novel paradigm of meta-learning approach with three developments to introduce attention mechanism and prior-knowledge for meta-learning.