Shot Learner

Shot learning, particularly few-shot learning, focuses on training machine learning models to effectively classify new data categories using only a limited number of examples. Current research emphasizes adapting large vision-language models (LVLMs) and other pre-trained models like CLIP for few-shot tasks, often employing meta-learning strategies, lightweight adapters, or prompt engineering techniques to improve performance and efficiency. This area is significant because it addresses the limitations of traditional machine learning approaches that require extensive labeled data, enabling applications in domains with scarce resources, such as medical diagnosis and rare language processing. Furthermore, research also investigates the robustness and security of these models against adversarial attacks.