EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor?
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910092316639232 |
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| author | Adorni, Pierre Pham, Minh-Tan May, Stéphane Lefèvre, Sébastien |
| author_facet | Adorni, Pierre Pham, Minh-Tan May, Stéphane Lefèvre, Sébastien |
| contents | Recent advances in foundation models have shown great promise in domains such as natural language processing and computer vision, and similar efforts are now emerging in the Earth Observation community. These models aim to generalize across tasks with limited supervision, reducing the need for training separate models for each task. However, current strategies, which largely focus on scaling model size and dataset volume, require prohibitive computational and data resources, limiting accessibility to only a few large institutions. Moreover, this paradigm of ever-larger models stands in stark contrast with the principles of sustainable and environmentally responsible AI, as it leads to immense carbon footprints and resource inefficiency. In this work, we present a novel and efficient alternative: an Ensemble-of-Specialists framework for building Remote Sensing Foundation Models (RSFMs). Our method decomposes the training process into lightweight, task-specific ConvNeXtV2 specialists that can be frozen and reused. This modular approach offers strong advantages in efficiency, interpretability, and extensibility. Moreover, it naturally supports federated training, pruning, and continuous specialist integration, making it particularly well-suited for collaborative and resource-constrained settings. Our framework sets a new direction for building scalable and efficient RSFMs. All codes and pretrained models are available on the public repo at https://github.com/pierreadorni/EoS-FM . |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_21523 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor? Adorni, Pierre Pham, Minh-Tan May, Stéphane Lefèvre, Sébastien Computer Vision and Pattern Recognition Recent advances in foundation models have shown great promise in domains such as natural language processing and computer vision, and similar efforts are now emerging in the Earth Observation community. These models aim to generalize across tasks with limited supervision, reducing the need for training separate models for each task. However, current strategies, which largely focus on scaling model size and dataset volume, require prohibitive computational and data resources, limiting accessibility to only a few large institutions. Moreover, this paradigm of ever-larger models stands in stark contrast with the principles of sustainable and environmentally responsible AI, as it leads to immense carbon footprints and resource inefficiency. In this work, we present a novel and efficient alternative: an Ensemble-of-Specialists framework for building Remote Sensing Foundation Models (RSFMs). Our method decomposes the training process into lightweight, task-specific ConvNeXtV2 specialists that can be frozen and reused. This modular approach offers strong advantages in efficiency, interpretability, and extensibility. Moreover, it naturally supports federated training, pruning, and continuous specialist integration, making it particularly well-suited for collaborative and resource-constrained settings. Our framework sets a new direction for building scalable and efficient RSFMs. All codes and pretrained models are available on the public repo at https://github.com/pierreadorni/EoS-FM . |
| title | EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.21523 |