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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 视觉细分将图像/视频分成有意义的组,用于自动驾驶和医学分析等应用.
    • 在过去的十年里,深度学习方法已经大大提升了视觉细分.
    • 最初用于NLP的变压器现在在视觉任务中表现出色,超过了卷积和循环网络.

    研究的目的:

    • 提供基于变压器的视觉细分方法的全面概述.
    • 总结最近的进展,并在一个元架构下统一最近的基于变压器的方法.
    • 探索特定的子领域,并确定未来的研究方向.

    主要方法:

    • 对背景的审查,包括问题定义,数据集和先前的卷积方法.
    • 基于变压器的视觉细分的统一元架构的总结.
    • 基于元架构的各种方法设计,修改和应用的检查.

    主要成果:

    • 变压器为各种细分任务提供了强大,统一和简单的解决方案.
    • 具体子领域的详细检查:3D点云细分,基础模型调整,域意识,高效和医疗细分.
    • 在已建立的数据集上重新评估已审查的方法.

    结论:

    • 基于变压器的方法代表了视觉细分的重大进步.
    • 该调查强调了关键的挑战,并提出了这个快速发展的领域的未来研究途径.