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Human beings can recognize new objects with only a few labeled examples,however, few-shot learning remains a challenging problem for machine learningsystems. Most previous algorithms in few-shot learning only utilize spatialinformation of the images. In this paper, we propose to integrate the frequencyinformation into the learning model to boost the discrimination ability of thesystem. We employ Discrete Cosine Transformation (DCT) to generate thefrequency representation, then, integrate the features from both the spatialdomain and frequency domain for classification. The proposed strategy and itseffectiveness are validated with different backbones, datasets, and algorithms.Extensive experiments demonstrate that the frequency information iscomplementary to the spatial representations in few-shot classification. Theclassification accuracy is boosted significantly by integrating features fromboth the spatial and frequency domains in different few-shot learning tasks.

Few-Shot Learning by Integrating Spatial and Frequency Representation