Adaptive blank compensation for few-shot image classification

Abstract

In recent years, few-shot learning has garnered significant attention from researchers. A notable trend in this domain involves the integration of feature distribution information from approximate base classes and the calibration of distributions stemming from a limited number of samples. However, the approximate classes found by existing methods are often not optimal. Through a limited number of samples in a novel class, approaching their class center is the key to determining the approximate base classes. We assume that the center of a novel class tends to be close to the center of the blank region between multiple approximate base classes. We verified this hypothesis and propose a calibration method based on it. With this method, a limited number of samples are compensated and calibrated in the direction close to the blank center of the approximate base class distribution. Finally, the more approximate base classes and more accurate distribution information can be obtained. Extensive experiments on miniImagenet, CUB, and CIFAR-FS datasets show that the proposed method achieves competitive performance. The code is available at https://github.com/DN-KID/ABC.