DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911163657224192 |
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| author | Yang, Meng Fan, Fan Li, Zizhuo Deng, Songchu Ma, Yong Ma, Jiayi |
| author_facet | Yang, Meng Fan, Fan Li, Zizhuo Deng, Songchu Ma, Yong Ma, Jiayi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_16017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching Yang, Meng Fan, Fan Li, Zizhuo Deng, Songchu Ma, Yong Ma, Jiayi Computer Vision and Pattern Recognition I.4.3; I.5.2 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. |
| title | DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching |
| topic | Computer Vision and Pattern Recognition I.4.3; I.5.2 |
| url | https://arxiv.org/abs/2509.16017 |