MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models
Vision Foundation Models (VFMs) have become the cornerstone of modern computer vision, offering robust representations across a wide array of tasks. While recent advances allow these models to handle varying input sizes during training, inference typically remains restricted to a single, fixed scale. This prevalent single-scale paradigm overlooks a fundamental property of visual perception: varying resolutions offer complementary inductive biases, where low-resolution views excel at global semantic recognition and high-resolution views are essential for fine-grained refinement. In this work, we propose Multi-Resolution Fusion (MuRF), a simple yet universally effective strategy to harness this synergy at inference time. Instead of relying on a single view, MuRF constructs a unified representation by processing an image at multiple resolutions through a frozen VFM and fusing the resulting features. The universality of MuRF is its most compelling attribute. It is not tied to a specific architecture, serving instead as a fundamental, training-free enhancement to visual representation. We empirically validate this by applying MuRF to a broad spectrum of critical computer vision tasks across multiple distinct VFM families - primarily DINOv2, but also demonstrating successful generalization to contrastive models like SigLIP2.
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Bocheng Zou
claimed_verified
2026-03-27T11:33:53.174Z
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/avatars/4edffaa045cc736e2edbf858fb6ef7c8.svg
Bocheng Zou
BochengZou
user
BochengZou
69c5fc552de2ee20094edc14
Mu Cai
69c5fc552de2ee20094edc15
Mark Stanley
claimed_verified
2026-03-27T11:33:51.100Z
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Mark Stanley
markstanl
user
markstanl
69c5fc552de2ee20094edc16
Dingfu Lu
69c5fc552de2ee20094edc17
Yong Jae Lee
MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models
Vision Foundation Models (VFMs) have become the cornerstone of modern computer vision, offering robust representations across a wide array of tasks. While recent advances allow these models to handle varying input sizes during training, inference typically remains restricted to a single, fixed scale. This prevalent single-scale paradigm overlooks a fundamental property of visual perception: varying resolutions offer complementary inductive biases, where low-resolution views excel at global semantic recognition and high-resolution views are essential for fine-grained refinement. In this work, we propose Multi-Resolution Fusion (MuRF), a simple yet universally effective strategy to harness this synergy at inference time. Instead of relying on a single view, MuRF constructs a unified representation by processing an image at multiple resolutions through a frozen VFM and fusing the resulting features. The universality of MuRF is its most compelling attribute. It is not tied to a specific architecture, serving instead as a fundamental, training-free enhancement to visual representation. We empirically validate this by applying MuRF to a broad spectrum of critical computer vision tasks across multiple distinct VFM families - primarily DINOv2, but also demonstrating successful generalization to contrastive models like SigLIP2.
Multi-Resolution Fusion enables vision foundation models to leverage complementary inductive biases from different resolutions without architectural modifications or additional training.
Vision Foundation Models multi-resolution fusion visual perception inductive biases DINOv2 SigLIP2