MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models

• URL: http://arxiv.org/abs/2603.25744v1
• Date: Thu, 26 Mar 2026 17:59:58 GMT
• Title: MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models
• Abstract summary: Multi-Resolution Fusion (MuRF) is a simple yet universally effective strategy to harness this synergy at inference time.<n>MuRF constructs a unified representation by processing an image at multiple resolutions through a frozen VFM.<n>We empirically validate this by applying MuRF to a broad spectrum of critical computer vision tasks across multiple distinct VFM families.
• Score: 34.365616310655575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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