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攻击增强混合对比的骨表示学习学习
概括
这项研究引入了针对无监督动作识别的攻击驱动框架,生成更难的正和负样本,以改进骨架表示和对比学习. 这种新的方法提高了模型的稳定性和性能.
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:
- 无监督动作识别依赖于对比学习,具有正负样本对.
- 现有的方法使用数据增强来构建对,但这些方法往往缺乏语义变化并模糊对边界.
- 骨架的随机外观变化无法捕捉有意义的语义差异,并削弱了对比的目标.
研究的目的:
- 开发一种攻击驱动的增强框架,用于在骨架数据中产生语义上有意义的扰动.
- 创建有信息的硬正和硬负样本,用于在无监督行动识别中进行强有力的表示学习.
- 引入一种新的框架,即攻击增强混合对比骨表示学习 (A^2MC),用于增强对比学习.
主要方法:
- 提出了一个攻击增强 (Att-Aug) 模块,集成有针对性的 (基于攻击) 和无针对性的 (基于增强) 扰动,以生成硬正样本.
- 引入了正负混合器 (PNM),通过混合硬正负特征来合成具有挑战性的硬负.
- 实施了一个混合记忆库,更新了合成的硬负面,以改善对比学习.
主要成果:
- 在A^2MC框架有效地产生语义丰富的硬正和硬负样本.
- 提出的方法显著提高了用于动作识别的骨架表示的稳定性.
- 根据三个基准的评估,A^2MC的表现与最先进的方法相竞争或优于它们.
结论:
- 攻击驱动增强为在对比学习中创建信息样本对提供了更有效的策略.
- A^2MC框架为无监督的基于骨架的行动识别提供了一种新且有效的方法.
- 该方法证明了明确的语义扰动对于推进表示学习的潜力.
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