无监督类生成以扩展语义细分数据集
Javier Montalvo1, Álvaro García-Martín1, Pablo Carballeira1
1Video Processing and Understanding Lab, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
Journal of imaging
|June 25, 2025
概括
本研究介绍了一种新的管道,使用稳定扩散和细分任何模块来生成语义细分的合成数据. 这种方法有效地对新型类进行细分,并以最小的用户输入改善整体模型性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 语义细分需要广泛的像素级标签,这使得它昂贵且耗时.
- 合成数据和域调整用于降低标签成本,但与新型类作斗争.
- 生成模型,特别是扩散模型,从文本提示中创建高质量的图像,无需监督.
研究的目的:
- 开发一种无监督的管道,用于生成新型类别的细分面具的合成数据.
- 将这些生成的例子集成到现有的语义细分数据集中.
- 通过在不改变核心算法的情况下加入新的类来提高无监督域的适应性.
主要方法:
- 利用稳定扩散用于基于文本提示的图像生成.
- 使用SegmentAnything模块进行自动面具生成.
- 开发一种方法,将创建的新类的切片集成到训练数据集中.
主要成果:
- 成功生成了类示例与相关的细分面具.
- 将新型类数据集成到语义细分数据集中,用户输入最小.
- 在新课程中实现了51%的平均交叉与联盟 (IoU) 交叉.
- 减少现有类的错误,从而提高整体性能.
结论:
- 拟议的无监督管道有效地产生和整合用于语义细分的新课程.
- 这种方法通过扩大培训数据多样性来增强无监督领域的适应性.
- 该方法显示了在没有广泛的手动注释或算法修改的情况下改善语义细分模型的巨大潜力.
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