M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915412933869568 |
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| author | Zhou, Kailai Yang, Fuqiang Wang, Shixian Wen, Bihan Zi, Chongde Chen, Linsen Shen, Qiu Cao, Xun |
| author_facet | Zhou, Kailai Yang, Fuqiang Wang, Shixian Wen, Bihan Zi, Chongde Chen, Linsen Shen, Qiu Cao, Xun |
| contents | 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. The code will be available at https://github.com/CalayZhou/M-SpecGene. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16318 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision Zhou, Kailai Yang, Fuqiang Wang, Shixian Wen, Bihan Zi, Chongde Chen, Linsen Shen, Qiu Cao, Xun Computer Vision and Pattern Recognition 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. The code will be available at https://github.com/CalayZhou/M-SpecGene. |
| title | M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.16318 |