在负面场景中的语义细分,标签图像较少,标签图像较少
Guanhua An1, Jichang Guo2, Chunle Guo3
1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
概括
这项研究引入了一种新的半监督学习方法,用于降解图像的语义细分,显著减少对标记数据的需求. 我们的方法实现了最先进的性能与最小的标签,使其高效的挑战视觉领域.
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:
- 来自不利场景的退化图像对诸如语义细分等高层视觉任务构成挑战.
- 手动标记这些图像是劳动密集型和不切实际的训练强大的模型.
研究的目的:
- 开发一种半监督式学习方法,用于对退化图像的语义细分,需要更少的标记样本.
- 为了解决传统的半监督方法的局限性,这些方法需要高比例的标记数据.
主要方法:
- 提出了一种新的两步培训管道,通过分离标记和未标记的图像培训来防止过度装配.
- 引入了重新参数化域适配器 (RPDA) 以对标记数据进行高效的域适配.
- 利用教师网络来提炼知识,并使用带有标签感知 (CATLP) 的类适应值来准确地在未标签的数据上生成伪标签.
主要成果:
- 拟议的方法在公开数据集上实现了优越的性能,相比于具有不利场景图像的最先进的方法.
- 以0.5%-1%的标记图像来证明有效性,这与传统方法相比大幅减少.
结论:
- 这种新型的半监督方法显著减轻了对标记数据的需求,用于降解图像的语义细分.
- 该方法为在具有挑战性的视觉环境中进行域调整提供了实用和高效的解决方案.
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