通过自适应注意力和关系来检测面部动作单元
概括
这项研究引入了一个适应性注意力和关系 (AAR) 框架,通过更好地捕捉微妙的,动态的AU相关性来改善面部动作单元 (AU) 检测. AAR框架在受约束和不受约束的面部表情识别场景中提高了准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 面部动作单元 (AU) 检测对于理解人类情绪和表情至关重要.
- 现有的方法与微妙的,动态的AU斗争,并且经常过度简化或错过关键的关系信息.
- 当前的方法可能会忽略面部的重要区域,或者在注意力机制中包含不相关的区域.
研究的目的:
- 开发一种新的自适应注意力和关系 (AAR) 框架,以改进面部AU检测.
- 解决从微妙和动态AU捕获相关信息的局限性.
- 提高每个非洲联盟的区域相关性分布学习的精度.
主要方法:
- 提出了适应性注意力回归网络,以学习每个AU的全球注意力地图,平衡基于地标和全球依赖.
- 开发了一个适应的时空图卷积网络,用于推理独立的,相互依赖的和时间的AU模式.
- 将这些组件集成到面部AU检测的统一AAR框架中.
主要成果:
- 在具有挑战性的基准上取得了竞争性表现:BP4D,DISFA,GFT (受限制) 和Aff-Wild2 (不受限制).
- 证明了对各个AU的区域相关分布的精确学习.
- 该AAR框架有效地捕捉了特定和全球分布的面部依赖性.
结论:
- 拟议的AAR框架在面部AU检测方面取得了重大进展.
- 适应性注意力和时空推理机制是处理AU复杂性的关键.
- 这种方法显示出在情绪识别和人与计算机交互方面的真实应用的巨大潜力.
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