Learning Multi-level Weight-centric Features for Few-shot Learning

Understand

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.