学习基于事件的运动细分的域不变表示:无监督域适应方法
1Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Mashhad 9177948974, Iran.
Journal of imaging
|November 26, 2025
概括
本研究引入了使用事件摄像头进行运动细分的新框架,成功地将RGB数据的知识适应到事件流中. 该方法在具有挑战性的基准测试上取得了最先进的结果,为高速应用提供了轻量级和高效的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 传感器融合式传感器
背景情况:
- 事件摄像机提供高时间分辨率和动态范围,非常适合高速应用.
- 事件数据的稀疏性和注释的缺乏阻碍了运动细分的监督学习.
- 由于强度图像和事件数据之间的显著转移,域名适应是困难的.
研究的目的:
- 开发一种跨模式适应框架,用于从事件流中对动作进行细分.
- 从标记的RGB流数据转移知识到未标记的事件数据.
- 为了应对数据稀疏性,注释稀缺性和基于事件的视觉领域转移的挑战.
主要方法:
- 双分支编码器从源域中的RGB和光流中提取特征.
- 重建网络将事件数据转换为目标域的伪图像和伪流模式.
- 多级一致性损失强制执行对特征,预测和输出进行域调整.
主要成果:
- 拟议的框架在EVIMO2上达到83.1%的准确性,在MOD++上达到79.4%.
- 它的性能高于EV-Transfer和SHOT等现有方法,高达3.6%.
- 展示了一个轻量级的架构,允许实时推断,并增强了mIoU和F1Score.
结论:
- 跨模式适应框架有效地弥合了基于事件的运动细分的领域和模式差距.
- 该方法使得获取域不变的,有语义丰富的特征,降低了培训成本.
- 这种方法适用于现实世界的高速应用,如自动驾驶.
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