Query-Guided Prototype Optimization for Few-Shot Classification

Jiaxing Sun, Yinghui Sun, Xiaobo Shen, Quansen Sun

Abstract: With limited labeled samples, few-shot classification poses a challenge to standard deep models and has attracted a surge of concern. Metric learning based approaches stand out for the minimalist and efficient design, aiming to classify query samples using supervision of support sets in the metric space. Prototypical Network has pioneered the use of mean feature embeddings to represent each support class and leaned on the computed prototypes for query classification. However, inherent bias exists in the mean prototypes generating from scarce support samples versus the actual class prototypes, which induces subsequent inference deviation. In this paper, we propose to diminish the bias by leveraging the semantic information of query samples to guide prototype optimization. Specifically, we exploit the semantic correlation between the local of initial mean prototypes and the global of query samples to generate query-guided masks, thus tailoring optimized prototypes that vary by query samples. This exploration of correlation is first utilized to alleviate the prototype bias problem and shows great brevity compared to existing methods. Extensive experiments are conducted on three few-shot image classification benchmark datasets, and demonstrate the effectiveness of our proposed method.