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Related Experiment Video

Updated: Jun 12, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Zero-shot prompt-based video encoder for surgical gesture recognition.

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
PubMed
Summary
This summary is machine-generated.

This study explores zero-shot learning for surgical gesture recognition using prompt-tuned vision-text models. The approach enables models to recognize new surgical gestures without retraining, proving invaluable for diverse robotic surgery applications.

Keywords:
Cross-task learningPrompt engineeringSurgical gesture recognitionZero-shot learning

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Area of Science:

  • Computer Vision
  • Robotic Surgery
  • Machine Learning

Background:

  • Surgical gesture recognition systems require extensive data or zero-shot capability for broad applicability.
  • Generalizing models to new surgical gestures is crucial for supporting diverse procedures.

Purpose of the Study:

  • Investigate the feasibility of zero-shot learning for surgical gesture recognition.
  • Develop a system capable of recognizing a wide variety of surgical procedures without task-specific retraining.

Main Methods:

  • Utilized the bridge-prompt framework to prompt-tune a pre-trained vision-text model (CLIP).
  • Incorporated extensive external video data, text, label metadata, and weakly supervised contrastive losses.
  • Employed prompt-based video encoders for gesture recognition tasks.

Main Results:

  • Prompt-based video encoders outperformed standard encoders in surgical gesture recognition.
  • Demonstrated strong performance in zero-shot scenarios, recognizing previously unseen gestures.
  • Quantified the benefits of including text descriptions in feature extractor training.

Conclusions:

  • Bridge-prompt and similar pre-trained + prompt-tuned models offer significant visual representation for surgical robotics.
  • The zero-shot transfer capability of these models is invaluable for diverse surgical tasks, eliminating the need for gesture-specific retraining.