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Meta-learning and other approaches to few-shot learning are widely studiedfor image recognition, and are increasingly applied to other vision tasks suchas pose estimation and dense prediction. This naturally raises the question ofwhether there is any few-shot meta-learning algorithm capable of generalizingacross these diverse task types? To support the community in answering thisquestion, we introduce Meta Omnium, a dataset-of-datasets spanning multiplevision tasks including recognition, keypoint localization, semanticsegmentation and regression. We experiment with popular few-shot meta-learningbaselines and analyze their ability to generalize across tasks and to transferknowledge between them. Meta Omnium enables meta-learning researchers toevaluate model generalization to a much wider array of tasks than previouslypossible, and provides a single framework for evaluating meta-learners across awide suite of vision applications in a consistent manner.

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