MetaReVision：面向视觉接地组合概念习得的带检索元学习
MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition
AI总结：
受人类借助原始概念学习新组合概念的机制启发，本文提出检索增强的元学习模型MetaReVision，构建两个基准数据集，实验证明其在视觉接地组合概念学习任务上优于基线，且检索模块作用关键。
摘要：
人类具备通过回忆并泛化从过往经验中习得的原始概念来学习新组合概念的能力。受此启发，本文提出MetaReVision——一种检索增强的元学习模型，用于解决视觉接地组合概念学习问题。该模型由检索模块和元学习模块构成，二者协同将检索到的原始概念作为支持集，对视觉-语言模型进行元训练，以实现接地组合概念识别。通过在检索器构建的学习片段上进行元学习，MetaReVision可习得通用的组合表征，该表征能被快速更新以识别新的组合概念。本文构建了CompCOCO和CompFlickr两个数据集，作为接地组合概念学习的基准。实验结果表明，MetaReVision性能优于其他有竞争力的基线模型，且检索模块在该组合学习过程中发挥着重要作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
