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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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LLaFS++:使用大型语言模型进行短拍图像细分.

Lanyun Zhu, Tianrun Chen, Deyi Ji

    IEEE transactions on pattern analysis and machine intelligence
    |May 26, 2025
    PubMed
    概括

    本研究介绍了LLaFS++,这是一种使用大型语言模型 (LLM) 改进少数镜头细分 (FSS) 的新框架. 通过利用LLMs来实现这一目标.

    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:

    • 短拍细分 (FSS) 方法在有限的标记数据中扎,阻碍了性能.
    • 现有的FSS方法依赖于小的,可能有偏见的数据集,限制了概括能力.

    研究的目的:

    • 引入LLaFS++,一个先进的框架,将大型语言模型 (LLM) 应用于少数镜头细分 (FSS).
    • 通过利用LLM的先前知识,克服在少数镜头标记样本中信息不足和偏见的局限性.

    主要方法:

    • LLaFS++集成了LLMs来指导FSS过程,补偿有限的样本信息.
    • 介绍了特定任务的设计:多边形输出指令,区域属性表用于多模式指导,伪样本合成,课程学习,以及一种新的推理方法来防止过分分割.
    • 利用LLM广泛的先验知识,提供卓越的细分指导.

    主要成果:

    • LLaFS++在基准数据集上取得了最先进的结果:PASCAL-$5^{i}$5i,COCO-$20^{i}$20i,以及FSS-1000.
    • 通过有效利用LLM指导,表现出显著的绩效改进.
    • 该框架通过其新的推断方法成功地减轻了过分分割的幻觉.

    结论:

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    • 通过成功整合大型语言模型,LLaFS++代表了少数拍摄细分的重大进步.
    • 拟议的框架展示了LLMs在解决少数镜头视觉任务方面的巨大潜力.
    • 这项工作通过将语言和视觉理解结合起来,为少量学习建立了一个新的方向.