Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn

Abstract

Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction. This naturally raises the question of whether there is any fewshot metalearning algorithm capable of generalizing across these diverse task types? To support the community in answering this question, we introduce Meta Omnium, a dataset-of-datasets spanning multiple vision tasks including recognition, keypoint localization, semantic segmentation and regression. We experiment with popular fewshot metalearning baselines and analyze their ability to generalize across tasks and to transfer knowledge between them. Meta Omnium enables metalearning researchers to evaluate model generalization to a much wider array of tasks than previously possible, and provides a single framework for evaluating meta-learners across a wide suite of vision applications in a consistent manner. Code and dataset are available at https://github.com/edi-meta-learning/meta-omnium.