标题： 多模态指代分割：综述 显示英文标题

标题： Multimodal Referring Segmentation: A Survey

摘要： 多模态指代分割旨在基于文本或音频格式的指代表达，在视觉场景（如图像、视频和3D场景）中分割目标对象。 这项任务在需要根据用户指令进行准确物体感知的实际应用中起着至关重要的作用。 在过去十年中，由于卷积神经网络、变压器和大型语言模型的进步，它在多模态领域获得了广泛关注，所有这些都显著提高了多模态感知能力。 本文对多模态指代分割进行了全面的综述。 我们首先介绍该领域的背景，包括问题定义和常用数据集。 接下来，我们总结了一个统一的元架构用于指代分割，并回顾了三种主要视觉场景（包括图像、视频和3D场景）中的代表性方法。 我们进一步讨论了广义指代表达（GREx）方法，以应对现实世界的复杂性挑战，以及相关任务和实际应用。 还提供了在标准基准上的广泛性能比较。 我们持续跟踪相关工作，网址为https://github.com/henghuiding/Awesome-Multimodal-Referring-Segmentation。 显示英文摘要

摘要： Multimodal referring segmentation aims to segment target objects in visual scenes, such as images, videos, and 3D scenes, based on referring expressions in text or audio format. This task plays a crucial role in practical applications requiring accurate object perception based on user instructions. Over the past decade, it has gained significant attention in the multimodal community, driven by advances in convolutional neural networks, transformers, and large language models, all of which have substantially improved multimodal perception capabilities. This paper provides a comprehensive survey of multimodal referring segmentation. We begin by introducing this field's background, including problem definitions and commonly used datasets. Next, we summarize a unified meta architecture for referring segmentation and review representative methods across three primary visual scenes, including images, videos, and 3D scenes. We further discuss Generalized Referring Expression (GREx) methods to address the challenges of real-world complexity, along with related tasks and practical applications. Extensive performance comparisons on standard benchmarks are also provided. We continually track related works at https://github.com/henghuiding/Awesome-Multimodal-Referring-Segmentation.