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

2023-05-12 · CVPR 2023 1 · 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.

📄 PDF Abstract BibTeX arXiv:2305.07625

Code (1)

edi-meta-learning/meta-omnium 공식 구현 pytorch

Tasks

Few-Shot Learning Meta-Learning Pose Estimation Semantic Segmentation

OmniUMI: Towards Physically Grounded Robot Learning via Human-Aligned Multimodal Interaction

2026-04-12 · Shaqi Luo, Yuanyuan Li, Youhao Hu, Chenhao Yu 외 arxiv

UMI-style interfaces enable scalable robot learning, but existing systems remain largely visuomotor, relying primarily on RGB observations and trajectory while providing only limited access to physical interaction signal…

General-Purpose In-Context Learning by Meta-Learning Transformers

2022-12-08 · Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, Luke Metz

Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims to learn those aspects, and …

In-Context Learning Inductive Bias Meta-Learning

MetaVLA: Unified Meta Co-training For Efficient Embodied Adaption

2025-10-07 · Chen Li, Zhantao Yang, Han Zhang, Fangyi Chen 외 arxiv

Vision-Language-Action (VLA) models show promise in embodied reasoning, yet remain far from true generalists-they often require task-specific fine-tuning, incur high compute costs, and generalize poorly to unseen tasks. …

Domain Generalization

Chemical Reaction Optimization for the Set Covering Problem

2015-02-01 · James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li

The set covering problem (SCP) is one of the representative combinatorial optimization problems, having many practical applications. This paper investigates the development of an algorithm to solve SCP by employing chemi…

Combinatorial Optimization

RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$

2023-06-28 · Abhinav Bhatia, Samer B. Nashed, Shlomo Zilberstein

Meta reinforcement learning (meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution. However, they show poor asymptotic perfor…

Meta Reinforcement Learning reinforcement-learning