[2605.11265] DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction
[Submitted on 11 May 2026]
Title:DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction
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Abstract:Dense prediction tasks in surgical computer vision, such as segmentation and surgical zone prediction, can provide valuable guidance for laparoscopic and robotic surgery. However, these models often suffer from distribution shifts, as training datasets rarely cover the variability encountered during deployment, leading to poor generalization. We propose DenseTRF, a self-supervised representation adaptation framework based on texture-centric attention. Our method leverages slot attention to learn texture-aware representations that capture invariant visual structures. By adapting these representations to the target distribution without supervision, DenseTRF significantly improves robustness to domain shifts. The framework is implemented through conditioning dense prediction on slot attention and model merging strategies. Experiments across multiple surgical procedures demonstrate improved cross-distribution generalization in comparison to state-of-the-art segmentation models and test-distribution adaptation methods for dense prediction tasks.
Comments: Accepted to 29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026)
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.11265 [cs.CV]
(or arXiv:2605.11265v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.11265
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Guiqiu Liao [view email] [v1] Mon, 11 May 2026 21:43:40 UTC (1,564 KB)
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