Shu-Tong Guo

SAEFormer: Self-Supervised and Attention-Enhanced Efficient Transformer for Robust Tomato Leaf Disease Recognition

This paper proposes SAEFormer, a lightweight and robust disease recognition model that integrates a Multi-scale Selective Fusion Attention Block to enhance the ability to model multi-scale semantic information in lesion areas and achieves competitive performance in cross-dataset evaluation.