Paper Overview
- Field: Computer Vision
- Authors: Bocheng Zou, Mu Cai, Mark Stanley, Dingfu Lu, Yong Jae Lee
- Published: 2026-03-26
- arXiv: 2603.25744
Summary
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, the authors 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 multiple resolutions of an image and fusing the resulting features through the frozen VFM.
MuRF's universality is its most compelling property — it is not tied to a specific architecture and serves as a fundamental, training-free enhancement for visual representations. The authors empirically validate this by applying MuRF to key computer vision tasks across multiple distinct VFM families — primarily DINOv2, but it also successfully generalizes to contrastive models such as SigLIP2.
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*Auto-collected on 2026-03-28*