可转移的无意动作本地化与语言引导的意图翻译翻译
概括
本研究引入了视频中无意动作本地化 (UAL) 的新框架. 它使用语言引导的意图翻译来改进非故意行为的检测,在开放场景中表现优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于需要解释微妙的意图线索,无意动作本地化 (UAL) 很困难.
- 现有的方法通常将UAL视为二进制分类,限制在开放式场景中的性能.
研究的目的:
- 为非故意动作本地化 (UAL) 开发一个新的框架,明确解决开放式场景.
- 通过利用语言引导的意图翻译来提高视频中非故意行为的理解和检测.
主要方法:
- 提出了一个可转移的非故意行动本地化框架,使用语言引导的意图翻译.
- 采用了变压器架构,用于在有意和无意的行动部分之间进行知识传输.
- 引入了一种密集的投票方案,用于检测使用歧视性表示的行动过渡.
主要成果:
- 与现有的UAL方法相比,拟议的框架在各种开放式场景中实现了更高的性能.
- 通过将框架扩展到竞技体育和一个新的数据集 (FS-Falls) 来证明了增强的概括能力.
结论:
- 语言引导意图翻译为UAL提供了一个强大的方法,在开放条件下提高性能.
- 该框架为创建具有完整行动意图优先级的表示提供了一个新的视角,以更好地理解人类行动.
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