Layer-Wise Adaptive Updating for Few-Shot Image Classification

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.

Classification Few-Shot Image Classification +2

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

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.

Face Anti-Spoofing Face Recognition +1

Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning

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.

Few-Shot Learning