多模态指称分割：综述
Multimodal Referring Segmentation: A Survey
AI总结：
本文综述多模态指称分割，涵盖问题定义、统一元架构、图像/视频/3D场景的代表方法及GREx方法，并给出基准性能比较。
摘要：
多模态指称分割旨在根据文本或音频形式的指称表达，在视觉场景（如图像、视频和3D场景）中分割目标对象。该任务在需要根据用户指令进行精确对象感知的实际应用中起着关键作用。在过去十年中，得益于卷积神经网络、Transformer和大语言模型的进步，多模态感知能力得到了显著提升，该任务在多模态社区中获得了广泛关注。本文对多模态指称分割进行了全面综述。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指称分割元架构，并回顾了在图像、视频和3D场景这三种主要视觉场景中的代表性方法。我们进一步讨论了广义指称表达（GREx）方法以应对现实世界复杂性的挑战，以及相关任务和实际应用。我们还提供了在标准基准上的广泛性能比较。我们持续在https://github.com/henghuiding/Awesome-Multimodal-Referring-Segmentation跟踪相关工作。
英文摘要：
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.
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。
