使用元测试时间调整进行人群计数
Chaoqun Ma1, Ferrante Neri2, Li Gu3
1School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, P. R. China.
International journal of neural systems
|September 10, 2024
概括
CrowdTTA通过使用元学习和测试时间调整来增强人群计数. 这种方法有效地使模型适应新的人群条件,而无需大量的培训数据.
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:
- 机器学习对于高效的人群计数至关重要.
- 当前的测试时间适应方法通常需要广泛的培训和未注释的数据.
- 无监督的域名适应是占主导地位的方法,需要大量资源.
研究的目的:
- 介绍CrowdTTA,一种新的元测试时间自适应群众计数方法.
- 为了使人群计数模型能够更有效地适应未知的测试分布.
- 减少对新目标域的大量未注释数据的依赖.
主要方法:
- 集成测试时间适应与群众计数的元学习.
- 通过dropout层引入不确定性,以生成像素级伪标签.
- 采用双层优化过程:内部自我监督更新和外部基本真相更新.
主要成果:
- 在不同的人群密度和规模的不同数据集中表现出一般的适应能力.
- 优于各种监督学习和领域适应方法.
- 有效地将模型适应未知的测试分布,以提高性能.
结论:
- CrowdTTA为人群计数提供了一个高效和适应性的解决方案.
- 超级学习框架与测试时间适应相结合,显著提高了模型的适应性.
- 这种方法为资源密集型域调整技术提供了可行的替代方案.
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