元组合指代表达分割
Meta Compositional Referring Expression Segmentation
AI总结：
本文提出元组合指代表达分割框架MCRES，通过构建虚拟训练集与多层级虚拟测试集并结合元优化，增强模型对概念新组合的泛化能力，在三个基准数据集上验证了有效性。
摘要：
指代表达分割旨在根据语言表达从图像中分割出所描述的对象。尽管该任务近期取得了进展，但现有模型可能无法充分捕捉各个概念的语义和视觉表示，这限制了它们的泛化能力，尤其是在处理已学习概念的新组合时。在本工作中，我们通过元学习的视角，提出了一种元组合指代表达分割（MCRES）框架，以增强模型的组合泛化性能。具体而言，为了处理不同层次的新组合，我们的框架首先利用训练数据构建一个虚拟训练集和多个虚拟测试集，其中每个虚拟测试集中的数据样本相对于虚拟训练集包含一个层次的新组合。然后，遵循一种新颖的元优化方案，在虚拟训练集上训练后优化模型，使其在虚拟测试集上获得良好的测试性能，我们的框架能够有效驱动模型更好地捕捉各个概念的语义和视觉表示，从而即使处理新组合时也能获得稳健的泛化性能。在三个基准数据集上的大量实验证明了我们框架的有效性。
英文摘要：
Referring expression segmentation aims to segment an object described by a language expression from an image. Despite the recent progress on this task, existing models tackling this task may not be able to fully capture semantics and visual representations of individual concepts, which limits their generalization capability, especially when handling novel compositions of learned concepts. In this work, through the lens of meta learning, we propose a Meta Compositional Referring Expression Segmentation (MCRES) framework to enhance model compositional generalization performance. Specifically, to handle various levels of novel compositions, our framework first uses training data to construct a virtual training set and multiple virtual testing sets, where data samples in each virtual testing set contain a level of novel compositions w.r.t. the virtual training set. Then, following a novel meta optimization scheme to optimize the model to obtain good testing performance on the virtual testing sets after training on the virtual training set, our framework can effectively drive the model to better capture semantics and visual representations of individual concepts, and thus obtain robust generalization performance even when handling novel compositions. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our framework.
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。
