MARCO: Navigating the Unseen Space of Semantic Correspondence
Summary
MARCO introduces a compact, fast model for semantic correspondence that achieves state-of-the-art accuracy and generalization to unseen keypoints using a coarse-to-fine objective and self-distillation framework with DINOv2.
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Paper page - MARCO: Navigating the Unseen Space of Semantic Correspondence
Source: https://huggingface.co/papers/2604.18267
Abstract
MARCO is a compact, fast model that improves semantic correspondence accuracy and generalization beyond training data by using a coarse-to-fine objective and self-distillation framework with DINOv2 and diffusion backbones.
Recent advances insemantic correspondencerely ondual-encoder architectures, combiningDINOv2withdiffusion backbones. While accurate, these billion-parameter models generalize poorly beyond training keypoints, revealing a gap between benchmark performance and real-world usability, where queried points rarely match those seen during training. Building uponDINOv2, we introduce MARCO, a unified model for generalizable correspondence driven by a novel training framework that enhances bothfine-grained localizationandsemantic generalization. By coupling acoarse-to-fine objectivethat refines spatial precision with aself-distillation framework, which expandssparse supervisionbeyond annotated regions, our approach transforms a handful of keypoints into dense, semantically coherent correspondences. MARCO sets a new state of the art on SPair-71k, AP-10K, and PF-PASCAL, with gains that amplify atfine-grained localizationthresholds (+8.9 [email protected]), strongest generalization to unseen keypoints (+5.1, SPair-U) and categories (+4.7, MP-100), while remaining 3x smaller and 10x faster than diffusion-based approaches. Code is available at https://github.com/visinf/MARCO .
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