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.

翻译：视觉基础模型（VFMs）已成为现代计算机视觉的基石，为广泛任务提供了鲁棒的表征。尽管近期进展允许这些模型在训练过程中处理不同大小的输入，但推理过程通常仍局限于单一固定尺度。这种普遍的单尺度范式忽视了视觉感知的一个基本特性：不同分辨率提供互补的归纳偏置，其中低分辨率视图擅长全局语义识别，而高分辨率视图对于细粒度优化至关重要。在本文中，我们提出多分辨率融合（MuRF），这是一种简单但普遍有效的策略，可在推理时利用这种协同效应。MuRF不依赖于单一视图，而是通过冻结的VFM以多分辨率处理图像，并融合所得特征，从而构建统一表征。MuRF的普适性是其最引人注目的特性。它不局限于特定架构，而是作为视觉表征的一种基础性、无需训练增强手段。我们通过将MuRF应用于多个不同VFM家族（主要是DINOv2，同时也成功泛化至对比学习模型如SigLIP2）的广泛关键计算机视觉任务，对此进行了实证验证。