  Purpose: Surgical video is an important data stream for gesture recognition.
Thus, robust visual encoders for those data-streams is similarly important.
Methods: Leveraging the Bridge-Prompt framework, we fine-tune a pre-trained
vision-text model (CLIP) for gesture recognition in surgical videos. This can
utilize extensive outside video data such as text, but also make use of label
meta-data and weakly supervised contrastive losses. Results: Our experiments
show that prompt-based video encoder outperforms standard encoders in surgical
gesture recognition tasks. Notably, it displays strong performance in zero-shot
scenarios, where gestures/tasks that were not provided during the encoder
training phase are included in the prediction phase. Additionally, we measure
the benefit of inclusion text descriptions in the feature extractor training
schema. Conclusion: Bridge-Prompt and similar pre-trained+fine-tuned video
encoder models present significant visual representation for surgical robotics,
especially in gesture recognition tasks. Given the diverse range of surgical
tasks (gestures), the ability of these models to zero-shot transfer without the
need for any task (gesture) specific retraining makes them invaluable.
