K-Shot Learning

A machine learning paradigm where a model must learn to classify objects using only k training examples per class. In the context of accessibility, k-shot learning is significant because it enables assistive technologies like personal object recognizers to be trained with minimal user effort — a blind user might provide just 5 or 20 photos of an object rather than the thousands typically required. Combined with transfer learning from pre-trained models, k-shot learning makes it practical for individuals to customize AI-powered tools to their specific needs.