  Few-shot learning is currently enjoying a considerable resurgence of
interest, aided by the recent advance of deep learning. Contemporary approaches
based on weight-generation scheme delivers a straightforward and flexible
solution to the problem. However, they did not fully consider both the
representation power for unseen categories and weight generation capacity in
feature learning, making it a significant performance bottleneck. This paper
proposes a multi-level weight-centric feature learning to give full play to
feature extractor's dual roles in few-shot learning. Our proposed method
consists of two essential techniques: a weight-centric training strategy to
improve the features' prototype-ability and a multi-level feature incorporating
a mid- and relation-level information. The former increases the feasibility of
constructing a discriminative decision boundary based on a few samples.
Simultaneously, the latter helps improve the transferability for characterizing
novel classes and preserve classification capability for base classes. We
extensively evaluate our approach to low-shot classification benchmarks.
Experiments demonstrate our proposed method significantly outperforms its
counterparts in both standard and generalized settings and using different
network backbones.
