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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:

    • 有限的数据集阻碍了计算机视觉模型在现实世界应用中的性能,例如野生动物观测.
    • 生成模型可以扩展数据集,但往往缺乏多样性和对输出的控制.
    • 现有的方法与自动,无法控制的图像生成作斗争,导致不需要的样本.

    研究的目的:

    • 为计算机视觉中可控数据集扩展提出一个由人类指导的方法.
    • 增强生成图像的多样性和相关性,以改善模型培训.
    • 解决自动生成模型在创建实用数据集中的局限性.

    主要方法:

    • 开发了一种多模式投影方法,用于探索原始和生成的图像.
    • 实施了样本级的快速改进技术,以获得用户指导的反.
    • 通过用户交互和快速调整,促进图像生成的代改进.

    主要成果:

    • 定量评估证实了多模式投影方法的有效性.
    • 提高计算机视觉模型在分类和物体检测任务中的性能.
    • 专家反验证了人类引导的生成过程的可用性和积极影响.

    结论:

    • 人类引导生成为计算机视觉中的数据集扩展提供了可控制的解决方案.
    • 提出的方法有效地提高了数据多样性和模型性能.
    • 样本级反简化了快速精细化,以提高图像生成质量.