Enabling Validation for Robust Few-Shot Recognition
Few-Shot Recognition (FSR) struggles because models lack validation data, leading to overfitting and poor generalization on new data. The paper introduces Validation-Enabled Stage-wise Tuning (VEST), a method that uses performance gains on both the original training data and retrieved external examples to guide hyperparameter tuning and layer selection, resulting in superior performance across in-distribution and out-of-distribution tests.