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arXiv · 2509.16017

DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching

Meng Yang·Fan Fan·Zizhuo Li·Songchu Deng·Yong Ma·Jiayi Ma
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Abstract

Multimodal image matching seeks pixel-level correspondences between images of different modalities, crucial for cross-modal perception, fusion and analysis. However, the significant appearance differences between modalities make this task challenging. Due to the scarcity of high-quality annotated datasets, existing deep learning methods that extract modality-common features for matching perform poorly and lack adaptability to diverse scenarios. Vision Foundation Model (VFM), trained on large-scale data, yields generalizable and robust feature representations adapted to data and tasks of various modalities, including multimodal matching. Thus, we propose DistillMatch, a multimodal image matching method using knowledge distillation from VFM. DistillMatch employs knowledge distillation to build a lightweight student model that extracts high-level semantic features from VFM (including DINOv2 and DINOv3) to assist matching across modalities. To retain modality-specific information, it extracts and injects modality category information into the other modality's features, which enhances the model's understanding of cross-modal correlations. Furthermore, we design V2I-GAN to boost the model's generalization by translating visible to pseudo-infrared images for data augmentation. Experiments show that DistillMatch outperforms existing algorithms on public datasets.

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Meng Yang, Fan Fan, Zizhuo Li, Songchu Deng, Yong Ma, Jiayi Ma. 2025-09-19. DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching. https://arxiv.org/abs/2509.16017

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