一种简单的预处理方法,用于改善无监督域调整中的语义细分
Shahaf Ettedgui1, Shady Abu-Hussein1, Raja Giryes2
1School of Electrical Engineering, Tel Aviv University, 69978, Tel Aviv, Israel.
Scientific reports
|July 2, 2025
概括
ProCST是一个新的预处理框架,使合成数据看起来像无监督域调整 (UDA) 的现实数据. 这种方法通过减少域间隙而提高语义细分性能,而不需要手动注释.
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:
- 无监督域调整 (UDA) 对于将在合成数据上训练的模型应用于真实世界的场景至关重要.
- 手动注释真实世界的数据是昂贵和耗时的,限制了监督学习的可扩展性.
- 弥合合成 (源) 和现实 (目标) 数据之间的领域差距是UDA的一个关键挑战.
研究的目的:
- 引入ProCST,这是一个用于无监督域调整 (UDA) 的新型预处理框架.
- 将源图像转换为类似目标图像,同时保留语义内容,以改进模型训练.
- 通过减少域差距和提高语义细分任务的性能来增强现有的UDA管道.
主要方法:
- ProCST采用一个多尺度的图像翻译架构.
- 使用一种独特的损失组合,包括循环标签损失,以保持语义结构和上下文.
- 该框架是作为预处理阶段设计的,可以无地集成到现有的UDA管道中.
主要成果:
- ProCST显著减少了合成数据和现实数据之间的域差距.
- 该方法在语义细分任务中实现了一致的性能增长.
- 在GTA5 →城市景观和工业废物细分挑战方面,观察到高达1.1%mIoU的改善,超过了当前最先进的结果.
结论:
- ProCST有效地生成具有高语义准确度的目标样图像,适合强大的模型训练.
- 该框架为语义细分中的域调整提供了具有成本效益的解决方案.
- ProCST促进了依赖于大规模注释数据的现实应用程序的发展.
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