Humans have the ability to learn novel compositional concepts by recalling
and generalizing primitive concepts acquired from past experiences. Inspired by
this observation, in this paper, we propose MetaReVision, a retrieval-enhanced
meta-learning model to address the visually grounded compositional concept
learning problem. The proposed MetaReVision consists of a retrieval module and
a meta-learning module which are designed to incorporate retrieved primitive
concepts as a supporting set to meta-train vision-anguage models for grounded
compositional concept recognition. Through meta-learning from episodes
constructed by the retriever, MetaReVision learns a generic compositional
representation that can be fast updated to recognize novel compositional
concepts. We create CompCOCO and CompFlickr to benchmark the grounded
compositional concept learning. Our experimental results show that MetaReVision
outperforms other competitive baselines and the retrieval module plays an
important role in this compositional learning process.

MetaReVision 是一个检索增强元学习模型，用于解决基于视觉感知的构成概念学习问题。该模型由检索模块和元学习模块组成，通过从已检索的基本概念中学习来支持元训练视觉 - 语言模型，以识别构成概念。MetaReVision 通过元学习从构建的检索集中学习出一种通用的构成表示，可以快速更新以识别新的构成概念。实验结果表明，MetaReVision 在构成学习过程中胜过其他竞争基准，并且检索模块在此过程中起重要作用。