语义解变压器与名词-动词嵌入用于组成动作识别.
概括
本研究介绍了DeFormer,这是一个构成性动作识别 (CAR) 的新框架,通过解对象和动作特征来解决分布转移. DeFormer显著提高了模型的概括性,并在基准数据集上取得了最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 复合动作识别 (CAR) 面临挑战,原因是动作对象对的分布变化.
- 现有的方法通常依赖于外部信息,无法消除固有的视频诱导偏见.
- 视觉上混乱的视频功能阻碍了概括,因为无法隔离对象.
研究的目的:
- 提出一种新的语义解变压器 (DeFormer),用于在高级特征中隐含的语义级别解对象-动作.
- 提高组合动作识别模型的概括能力.
- 通过解决固有的诱导偏见,克服以前CAR方法的局限性.
主要方法:
- 德福默框架有两个子模块:对象-运动解器 (OMD) 和语义解约束器 (SDC).
- OMD以注释先验初始化可学习的令牌,以将实例级表示解为外观和运动特征.
- SDC使用文本信息进行双对比关联,以约束脱的特征.
主要成果:
- 在STH-ELSE和EPIC-KITCHENS-55数据集上,DeFormer显示了显著的概括改进.
- 与基线相比,STH-ELSE的绝对改善率为3%至5.4%,EPIC-KITCHENS-55的绝对改善率为4.7%至9.2%.
- 使用基本真相和检测到的注释,获得了最先进的结果.
结论:
- 德福默有效地解决了组合动作识别中的分布转移问题.
- 拟议的语义级解方法增强了模型的概括性.
- 德福默在最先进的CAR研究中取得了重大进展.
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