Shot Image Classification

Few-shot image classification aims to train image classifiers using very limited labeled data, addressing the challenge of data scarcity in many real-world applications. Current research heavily focuses on leveraging pre-trained vision-language models (like CLIP), meta-learning techniques, and the development of novel loss functions and feature selection methods (e.g., using local descriptors and transformer architectures) to improve classification accuracy and robustness. This field is significant because it enables efficient adaptation of models to new visual concepts with minimal training data, impacting various domains including medical image analysis, remote sensing, and personalized AI systems.