MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching

• URL: http://arxiv.org/abs/2501.11299v1
• Date: Mon, 20 Jan 2025 06:56:30 GMT
• Title: MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching
• Authors: Yepeng Liu, Zhichao Sun, Baosheng Yu, Yitian Zhao, Bo Du, Yongchao Xu, Jun Cheng,
• Abstract summary: Keypoint detection and description methods often struggle with multimodal data. We propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching.
• Score: 54.740256498985026
• License:
• Abstract: Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness against the non-linear variations present in multimodal data. Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors. However, acquiring such data is often costly and impractical in many real-world scenarios. To address this challenge, we propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching using only single-modality training data. Specifically, we propose a novel latent feature aggregation module and a cumulative hybrid aggregation module to enhance the base keypoint descriptors trained on single-modality data by leveraging pre-trained features from Stable Diffusion models. We validate our method with recent keypoint detection and description methods in three multimodal retinal image datasets (CF-FA, CF-OCT, EMA-OCTA) and two remote sensing datasets (Optical-SAR and Optical-NIR). Extensive experiments demonstrate that the proposed MIFNet is able to learn modality-invariant feature for multimodal image matching without accessing the targeted modality and has good zero-shot generalization ability. The source code will be made publicly available.

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