Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Ondrej Bohdal Yinbing Tian Yongshuo Zong Ruchika Chavhan Da Li Henry Gouk Li Guo Timothy Hospedales
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 few-shot meta-learning 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 few-shot meta-learning
baselines and analyze their ability to generalize across tasks and to transfer
knowledge between them. Meta Omnium enables meta-learning 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.