DenseTRF: Texture-Aware Adaptation for Surgical Scene Prediction
A novel self-supervised approach named DenseTRF has been introduced to enhance dense prediction tasks within surgical computer vision, particularly for segmentation and surgical zone forecasting, which are essential in laparoscopic and robotic surgeries. This technique tackles distribution shifts that lead to inadequate generalization when models face variability absent from training datasets. DenseTRF leverages slot attention to develop texture-aware representations that maintain invariant visual structures, adjusting them to target distributions autonomously. The framework bases dense prediction on slot attention and incorporates model merging techniques. Experiments conducted across various surgical procedures demonstrate superior cross-distribution generalization compared to current methodologies. This research is documented in arXiv:2605.11265.
Key facts
- DenseTRF is a self-supervised representation adaptation framework
- It focuses on texture-centric attention using slot attention
- Aims to improve robustness to domain shifts in surgical scene dense prediction
- Addresses tasks like segmentation and surgical zone prediction
- Experiments conducted across multiple surgical procedures
- Framework conditions dense prediction on slot attention and model merging
- Published on arXiv with ID 2605.11265
- Targets laparoscopic and robotic surgery guidance
Entities
Institutions
- arXiv