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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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基于零拍摄提示的视频编码器用于手术手势识别.

Mingxing Rao1, Yinhong Qin1, Soheil Kolouri1

  • 1Department of Computer Science, Vanderbilt University, Nashville, USA.

International journal of computer assisted radiology and surgery
|September 17, 2024
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概括

这项研究探讨了使用提示调整的视觉-文本模型进行手术手势识别的零射击学习. 这种方法使模型能够在不需要再培训的情况下识别新的外科手势,这对于各种机器人手术应用来说是无价的.

关键词:
交叉任务学习学习提示工程是指快速的工程.手术手势识别手术手势识别零射击学习的学习.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人手术 机器人手术
  • 机器学习 机器学习

背景情况:

  • 手术手势识别系统需要广泛的数据或零射击能力才能广泛应用.
  • 将模型推广到新的外科手势对于支持各种程序至关重要.

研究的目的:

  • 研究用于手术手势识别的零射击学习的可行性.
  • 开发一个能够识别各种各样的外科手术程序的系统,而无需对特定任务进行再培训.

主要方法:

  • 使用桥式提示框架来提示调整预训练的视觉文本模型 (CLIP).
  • 整合了大量的外部视频数据,文本,标签元数据,以及监督较弱的对比损失.
  • 使用基于提示符的视频编码器来执行手势识别任务.

主要成果:

  • 基于提示的视频编码器在手术手势识别方面表现优于标准编码器.
  • 在零射击场景中表现出强的表现,识别以前未见的手势.
  • 量化了包括文本描述在特征提取器培训中的好处.

结论:

  • 桥式提示符和类似的预训练+即时调整模型为手术机器人提供了重要的视觉表示.
  • 这些模型的零射击传输能力对于各种手术任务是非常宝贵的,消除了对手势特定再培训的需求.