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Author

Matjaž Jogan

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DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction

This work proposes DenseTRF, a self-supervised representation adaptation framework based on texture-centric attention that leverages slot attention to learn texture-aware representations that capture invariant visual structures and significantly improves robustness to domain shifts.

Gui-Qiu Liao, Matjaž Jogan, Daniel A. Hashimoto · 0 citations
#computer vision Preprint Aug 2026

Slot2Text: Object-Centric Visual Tokenization for Efficient and Spatially Traceable Surgical MLLMs

Slot2Text, a dual-mode surgical MLLM that replaces dense representations of visual input with a compact set of regions encoded as slot latents, establishes compact slot latents as an efficient default visual interface for surgical MLLMs, with grounded reasoning invoked when greater spatial traceability is required.

Guiqiu Liao, M. Jogan, Daniel A. Hashimoto · 0 citations

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