Shot Data

Few-shot learning tackles the challenge of training machine learning models with limited labeled data, aiming to improve model adaptability and efficiency. Current research focuses on enhancing existing models like Vision-Language Models (VLMs) and Vision Transformers (ViTs) through techniques such as retrieval-augmented learning, adapter modules, and higher-order statistic calibration to improve performance in various tasks, including image classification, audio-visual acoustics modeling, and natural language processing. These advancements are significant because they enable the development of more robust and adaptable AI systems that can learn effectively from scarce resources, impacting fields ranging from robotics and anomaly detection to natural language understanding.