Generalized Few-Shot Classification

Generalized Few-Shot Classification is a learning paradigm in the field of computer vision that aims to achieve rapid recognition and classification of new categories through a small number of samples. This approach not only focuses on the generalization ability of known categories but also emphasizes adaptability to unknown categories, thereby enhancing the robustness and flexibility of models in real-world applications. Its core objective is to develop learning algorithms that can effectively leverage limited labeled data, reducing the reliance on large-scale datasets and increasing the practical value of the models.