  Recently, meta-learning has been shown as a promising way to solve few-shot
learning. 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. In our approach,
prior-knowledge is responsible for helping meta-learner expressing the input
data into high-level representation space, and attention mechanism enables
meta-learner focusing on key features of the data in the representation space.
Compared with existing meta-learning approaches that pay little attention to
prior-knowledge and vision attention, our approach alleviates the
meta-learner's few-shot cognition burden. Furthermore, a Task-Over-Fitting
(TOF) problem, which indicates that the meta-learner has poor generalization on
different K-shot learning tasks, is discovered and we propose a Cross-Entropy
across Tasks (CET) metric to model and solve the TOF problem. Extensive
experiments demonstrate that we improve the meta-learner with state-of-the-art
performance on several few-shot learning benchmarks, and at the same time the
TOF problem can also be released greatly.
