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    基于查询的端到端实例细分 (QEIS) 模型在有限的数据中扎. 使用语言视觉提示的无监督预训练 (UPLVP) 通过利用语言视觉提示来改进内核训练来提高低数据场景中的QEIS性能.

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    科学领域:

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

    背景情况:

    • 基于查询的端到端实例细分 (QEIS) 方法,灵感来自DETR,在大型数据集中表现出色,但在有限的数据中却失败.
    • 低数据模式中的性能下降是由于需要大量数据来训练可捕捉本地化和形状先验的关键查询/内核.

    研究的目的:

    • 开发一种新的无监督预培训方法,以提高在低数据制度中的QEIS模型性能.
    • 通过结合语言视觉提示来解决QEIS模型的数据依赖性.

    主要方法:

    • 建议使用语言视觉提示 (UPLVP) 的无监督预训,这是预训 QEIS 模型的三部分方法.
    • 面具建议:使用语言视觉模型从未标记的图像生成伪面具.
    • 提示符与内核匹配和内核监督:将伪面具转换为提示符,将功能与内核匹配,并应用内核级监督来实现强大的学习.

    主要成果:

    • 在低数据设置中,UPLVP使 QEIS 模型能够更快地融合,并与基于 CNN 的模型相比获得更好的性能.
    • 在MS COCO,Cityscapes和CTW1500数据集上的实验验证表明,使用UPLVP预训练的QEIS模型的性能有了显著的改善.

    结论:

    • 使用语言视觉提示 (UPLVP) 的无监督预训有效地增强了低数据制度中的 QEIS 模型.
    • 提出的方法克服了传统QEIS方法的数据限制,为缺乏培训数据的场景提供了可行的解决方案.