基于类原型对比的平均教师对域自适应对象检测的对比
Fukang Zhang1, Shanshan Gao2, Zheng Liu1
1School of Computer Science and Artificial Intelligence, Shandong University of Finance and Economics, Jinan, 250014, China.
概括
本研究引入了无监督域自适应对象检测 (UDAOD) 的新框架,以改善不同数据集的模型性能. 拟议的方法增强了特征对齐和伪标签,以实现更准确的对象检测.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 无监督域自适应对象检测 (UDAOD) 将在标记源数据上训练的模型应用于未标记的目标数据.
- 平均教师框架在UDAOD中很常见,但由于领域差异,它与错误的阳性和不完整的伪标签作斗争.
研究的目的:
- 提出一个新的学生-教师框架,原型对比平均教师 (PCMT),以提高UDAOD的表现.
- 解决跨领域对象检测中特征差异和伪标签不足的挑战.
主要方法:
- PCMT使用类原型进行对比学习,以保持类内特征和跨域调整特征.
- 基于界限框定位的伪标签过方法被引入以提高标签质量.
主要成果:
- 在各种域适应条件中,PCMT在UDAOD中表现出卓越的性能.
- 在Cityscapes → BDD100K数据集中,PCMT实现了43.5%的mAP,表现比最先进的高出5.0%.
结论:
- 拟议的PCMT框架有效地改善了无监督域自适应对象检测.
- PCMT对类原型和伪标签的方法在跨域计算机视觉任务中取得了重大进展.
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