MARCO: Navigating the Unseen Space of Semantic Correspondence

• URL: http://arxiv.org/abs/2604.18267v1
• Date: Mon, 20 Apr 2026 13:44:46 GMT
• Title: MARCO: Navigating the Unseen Space of Semantic Correspondence
• Abstract summary: MARCO is a unified model for generalizable correspondence driven by a novel training framework.<n>Our approach transforms a handful of keypoints into dense, semantically coherent correspondences.<n>MARCO sets a new state of the art on SPair-71k, AP-10K, and PF-PASCAL.
• Score: 28.55177516749558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion 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 upon DINOv2, we introduce MARCO, a unified model for generalizable correspondence driven by a novel training framework that enhances both fine-grained localization and semantic generalization. By coupling a coarse-to-fine objective that refines spatial precision with a self-distillation framework, which expands sparse supervision beyond 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 at fine-grained localization thresholds (+8.9 PCK@0.01), 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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