Few Shot Learning Approach for Object Recognition Transformation

Few-shot learning (FSL) is transforming object recognition, allowing models to recognize new objects with minimal data, addressing the issue of data scarcity in fields like medical imaging and wildlife monitoring. This paper implements a Prototypical Network model trained on the Omniglot dataset to evaluate its performance in recognizing characters from limited examples. Using metric-based learning and data augmentation, the model demonstrated robust accuracy in identifying unseen objects. Our results show promise for real-world applications, highlighting few-shot learning’s adaptability in low-data environments.