M-SpecGene

M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision (ICCV 2025)

Brief Introduction

RGB-Thermal (RGBT) multispectral vision is essential for robust perception in complex environments. Most RGBT tasks follow a case-by-case research paradigm, relying on manually customized models to learn task-oriented representations. Nevertheless, this paradigm is inherently constrained by artificial inductive bias, modality bias, and data bottleneck. To address these limitations, we make the initial attempt to build a Generalized RGBT MultiSpectral foundation model (M-SpecGene), which aims to learn modality-invariant representations from large-scale broad data in a self-supervised manner. M-SpecGene provides new insights into multispectral fusion and integrates prior case-by-case studies into a unified paradigm. Considering the unique characteristic of information imbalance in RGBT data, we introduce the Cross-Modality Structural Sparsity (CMSS) metric to quantify the information density across two modalities. Then we develop the GMM-CMSS progressive masking strategy to facilitate a flexible, easy-to-hard, and object-centric pre-training process. Comprehensive experiments validate M-SpecGene’s generalizability across eleven datasets for four RGBT downstream tasks.

RGBT550K Dataset

To pretrain a multispectral foundation model with robust generalization capabilities, we exert our utmost efforts to make a comprehensive collection of available RGBT datasets. The multispectral (RGBT) image datasets can be found at A Summary of Multispectral (RGBT) Image Datasets. Our meticulous collection and preprocessing yields RGBT550K, a comprehensive dataset comprising 548,238 high-quality samples. It encompasses diverse scenarios, tasks, lighting conditions, resolutions, and object categories, providing a solid foundation for the self-supervised pre-training of the multispectral foundation model. You can download the RGBT550K dataset from Baidu Cloud (code: rwf7) or One Dirve (to be updated).

`RGBT550K Usage`
sudo apt install p7zip-full
7z x RGBT550K_archive.7z.partaa

Pretrained Models

Pretrain	Backbone	Model Weights
M-SpecGene	ViT-S
M-SpecGene	ViT-B

Usage

Pretraining

Finetuning

1. RGBT Multispectral Object Detection

2. RGBT Multispectral Semantic Segmentation

3. RGBT Cross-modality Feature Matching

4. RGBT Multispectral Salient Object Detection

Citation

@article{zhou2025m,
  title={M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision},
  author={Zhou, Kailai and Yang, Fuqiang and Wang, Shixian and Wen, Bihan and Zi, Chongde and Chen, Linsen and Shen, Qiu and Cao, Xun},
  journal={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2025}
}