PSMamba: 逐步自监督视觉Mamba用于植物疾病识别
PSMamba: Progressive Self-supervised Vision Mamba for Plant Disease Recognition
AI总结：
PSMamba通过结合视觉Mamba和双学生层次化蒸馏策略，提升植物疾病识别的准确性和鲁棒性。
摘要：
自监督学习（SSL）已成为一种强大的范式，用于在没有人工标注的情况下进行表示学习。然而，大多数现有框架专注于全局对齐，难以捕捉植物疾病图像中层次化、多尺度病斑模式的特征。为了解决这一差距，我们提出了PSMamba，一种逐步自监督框架，它将高效的序列建模能力与双学生层次化蒸馏策略相结合。与传统单一教师-学生设计不同，PSMamba采用共享的全局教师和两个专门的学生：一个处理中尺度视图以捕捉病斑分布和叶脉结构，另一个专注于局部视图以捕捉细粒度线索，如纹理不规则性和早期病变。这种多粒度监督促进了上下文和细节表示的联合学习，一致性损失确保了跨尺度对齐的连贯性。在三个基准数据集上的实验表明，PSMamba在域偏移和细粒度场景中均优于最先进的SSL方法，实现了更高的准确性和鲁棒性。
英文摘要：
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.
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