Few-Shot Image Retrieval in FiftyOne: Mining Edge Cases from Partially Labeled Data

The Few-Shot Learning plugin in FiftyOne Labs is designed to enhance image retrieval and identify model failures or niche cases within partially labeled or unlabeled datasets by leveraging a binary notion of similarity, where users label a small set of positive and negative samples. The plugin employs embeddings from models like ResNet, CLIP, and DINOv2, utilizing a prototype-style scoring system to rank the rest of the dataset. This lightweight workflow allows for rapid iteration by sampling and stabilizing working subsets, making it particularly useful for mining edge cases and growing niche classes. It simplifies the process by training a Rocchio-inspired prototype classifier, which writes back predictions to the dataset, enabling users to refine and improve their models iteratively within the FiftyOne App's interactive environment.