Title: DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: multimodal image matching, vision foundation model, knowledge distillation, modality-specific information

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 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.

TL;DR: DistillMatch uses knowledge distillation from vision foundation models for multimodal image matching. It employs a lightweight student model and a Category-Enhanced Feature Guidance Module. V2I-GAN is proposed for data augmentation.