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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.14309 |
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| _version_ | 1866917148492824576 |
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| author | Mamun, Abdullah Al Zhang, Miaohua Ahmedt-Aristizabal, David Hayder, Zeeshan Awrangjeb, Mohammad |
| author_facet | Mamun, Abdullah Al Zhang, Miaohua Ahmedt-Aristizabal, David Hayder, Zeeshan Awrangjeb, Mohammad |
| contents | Self-supervised Learning (SSL) has become a powerful paradigm for representation learning without manual annotations. However, most existing frameworks focus on global alignment and struggle to capture the hierarchical, multi-scale lesion patterns characteristic of plant disease imagery. To address this gap, we propose PSMamba, a progressive self-supervised framework that integrates the efficient sequence modelling of Vision Mamba (VM) with a dual-student hierarchical distillation strategy. Unlike conventional single teacher-student designs, PSMamba employs a shared global teacher and two specialised students: one processes mid-scale views to capture lesion distributions and vein structures, while the other focuses on local views to capture fine-grained cues such as texture irregularities and early-stage lesions. This multi-granular supervision facilitates the joint learning of contextual and detailed representations, with consistency losses ensuring coherent cross-scale alignment. Experiments on three benchmark datasets show that PSMamba consistently outperforms state-of-the-art SSL methods, delivering superior accuracy and robustness in both domain-shifted and fine-grained scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PSMamba: Progressive Self-supervised Vision Mamba for Plant Disease Recognition Mamun, Abdullah Al Zhang, Miaohua Ahmedt-Aristizabal, David Hayder, Zeeshan Awrangjeb, Mohammad Computer Vision and Pattern Recognition Self-supervised Learning (SSL) has become a powerful paradigm for representation learning without manual annotations. However, most existing frameworks focus on global alignment and struggle to capture the hierarchical, multi-scale lesion patterns characteristic of plant disease imagery. To address this gap, we propose PSMamba, a progressive self-supervised framework that integrates the efficient sequence modelling of Vision Mamba (VM) with a dual-student hierarchical distillation strategy. Unlike conventional single teacher-student designs, PSMamba employs a shared global teacher and two specialised students: one processes mid-scale views to capture lesion distributions and vein structures, while the other focuses on local views to capture fine-grained cues such as texture irregularities and early-stage lesions. This multi-granular supervision facilitates the joint learning of contextual and detailed representations, with consistency losses ensuring coherent cross-scale alignment. Experiments on three benchmark datasets show that PSMamba consistently outperforms state-of-the-art SSL methods, delivering superior accuracy and robustness in both domain-shifted and fine-grained scenarios. |
| title | PSMamba: Progressive Self-supervised Vision Mamba for Plant Disease Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.14309 |