Few-Shot Image Classification

Few-Shot Image Classification is a computer vision task aimed at training machine learning models to classify new images using only a few labeled samples (typically fewer than 6). The goal of this task is to enable the model to quickly recognize and classify new categories with minimal supervision and data requirements, thereby enhancing its generalization capability under conditions of limited data. This technology holds significant practical value, especially in scenarios where data acquisition is challenging or expensive.