Prototype-Based Self-Adaptive Distribution Calibration for Few-Shot Image Classification

Deep learning has flourished in large-scale supervised tasks. However, in many practical conditions, rich and available labeled data are a luxury. Thus, few-shot learning (FSL) has recently received boosting interest and achieved significant progress, which can learn new classes from several labeled samples. The advanced distribution calibration approach estimates the ground-truth distribution of few-shot classes by reusing the statistics of auxiliary data. However, there is still a significant discrepancy between the estimated distributions and ground-truth distributions, and artificially set hyperparameters cannot be adapted to different application scenarios (i.e., datasets). This paper proposes a prototype-based self-adaptive distribution calibration framework for estimating ground-truth distribution accurately and self-adaptive hyperparameter optimization for different application scenarios. Specifically, the proposed method is divided into two components. The prototype-based representative mechanism is for obtaining and utilizing more global information about few-shot classes and improving classification performance. The self-adaptive hyperparameter optimization algorithm searches robust hyperparameters for the distribution calibration of different application scenarios. The ablation studies verify the effectiveness of the various components of the proposed framework. Enormous experiments are conducted on three standard benchmarks such as miniImageNet, CUB-200-2011, and CIFAR-FS. The competitive results and compelling visualizations indicate that the proposed framework achieves state-of-the-art performance.