用感知一致性匹配用于语义细分的视频域调整.
Ihsan Ullah1, Sion An2, Myeongkyun Kang2
1Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea; Division of Intelligent Robotics, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea.
概括
本研究引入了一种用于无监督域调整的新方法,用于视频语义细分,在没有光流的情况下对视频进行对齐. 感知一致性匹配策略提高了视频-UDA任务的准确性和推断速度.
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:
- 无监督域调整 (UDA) 将知识从标记的源数据集转移到未标记的目标数据集.
- 与基于图像的UDA不同,基于视频的UDA由于复杂的模态特征和时间动态而具有挑战性.
- 现有的方法通常依赖于光流,这在计算上昂贵,并且很难在各个领域进行概括.
研究的目的:
- 为视频语义细分开发一种有效的无监督域调整方法.
- 解决现有方法的局限性,特别是对光流的依赖.
- 提高视频领域适应知识转移的准确性和效率.
主要方法:
- 为视频语义细分提出了一个对抗性域调整方法.
- 引入感知一致性匹配 (PCM) 以在没有光流的域中对准暂时关联的像素.
- 利用感知相似性来识别和强制执行连续中对应的像素的一致性.
主要成果:
- 拟议的PCM策略提高了视频UDA的预测准确性.
- 对公共数据集的现有最先进的UDA方法实现了显著的性能改进.
- 与基于光流的方法相比,表现出更快的推断时间.
结论:
- 开发的方法有效地解决了视频领域适应的关键任务.
- 对于视频-UDA,PCM为光学流提供了一个计算效率高,准确的替代方案.
- 该方法显示了对需要强大的视频语义细分的现实应用的巨大潜力.
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