SRCD:用复合域进行语义推理,用于单域通用对象检测
IEEE transactions on neural networks and learning systems
|November 5, 2024
概括
本研究引入了单域通用对象检测 (Single-DGOD) 的新框架,以改善模型通用化. 它增强了从增强数据中学习语义结构,克服了现有方法的局限性,以获得更好的跨领域性能.
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:
- 由于源数据有限,单域通用对象检测 (单DGOD) 具有挑战性.
- 现有的方法在伪属性-标签相关性方面扎,并忽略了关键的语义结构信息.
- 实例级语义关系对于强大的模型通用化至关重要.
研究的目的:
- 为单个DGOD提出一个新的框架,即含义推理与复合域 (SRCD),用于单个DGOD.
- 通过学习和维护自我增强的复合跨域样本的语义结构来增强模型概括.
- 解决现有方法在处理稀缺的单域数据和语义结构信息方面的局限性.
主要方法:
- 引入了单个DGOD的语义推理与复合域 (SRCD) 框架.
- 开发了一个基于纹理的自我增强 (TBSA) 模块,以消除无关紧要的属性效应.
- 实现了局部-全球语义推理 (LGSR) 模块,以建模实例级语义关系并保留内在结构.
主要成果:
- 拟议的SRCD框架显著提高了单一DGOD任务的概括能力.
- 在多个基准上的实验验验证了SRCD在提高对象检测性能方面的有效性.
- TBSA和LGSR模块有效地解决了以前方法的局限性.
结论:
- 对于具有挑战性的单一DGOD问题,SRCD框架提供了一个有希望的解决方案.
- 通过自我增强和推理来学习和维护语义结构是强大的概括的关键.
- 拟议的方法推进了领域通用物体检测的最新技术.
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