首页|Learning multi-level weight-centric features for few-shot learning
Learning multi-level weight-centric features for few-shot learning
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NSTL
Elsevier
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.(c) 2022 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
