Few-Shot Image Classification

Few-shot classification is highly valuable in domains where annotation is expensive or difficult. In tasks such as medical imaging, industrial defect types, and rare-species recognition, collecting many examples per class may be impossible. This approach aims to learn general representations first and then adapt rapidly to new classes from only a handful of examples.