Learning Primitive-aware Discriminative Representations for FSL
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Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to recognize novel classes,given only a few labeled examples per class.Limited data keep this task challenging for deep learning.Recent metric-based methods has achieved promising performance based on image-level features.However,these global features ignore abundant local and structural information that is transferable and consistent between seen and unseen classes.Some study in cognitive science argue that humans can recognize novel classes with the learned primitives.We expect to mine both transferable and discriminative representation from base classes and adopt them to recognize novel classes.Building on the episodic training mechanism,We propose a Primitive Mining and Reasoning Network(PMRN) to learn primitive-aware representation in an end-to-end manner for metric-based FSL model.We first add self-supervision auxiliary task,forcing feature extractor to learn tvisual pattern corresponding to primitives.To further mine and produce transferable primitive-aware representations,we design an Adaptive Channel Grouping(ACG)module to synthesize a set of visual primitives from object embedding by enhancing informative channel maps while suppressing useless ones. Based on the learned primitive feature,a Semantic Correlation Reasoning (SCR) module is proposed to capture internal relations among them.Finally,we learn the task-specific importance of primitives and conduct primitive-level metric based on the task-specific attention feature.Extensive experiments show that our method achieves state-of-the-art results on six standard benchmarks.
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