Multi-subspace mapping and adaptive learning: MMAL-CL for cross-domain few-shot image identification across scenarios

Image detection plays a critical role in quality control across manufacturing and healthcare sectors, yet existing methods struggle to meet real-world requirements due to their heavy reliance on large labeled datasets, poor generalization across different domains, and limited adaptability to diverse application scenarios. These limitations significantly hinder the deployment of AI solutions in practical industrial settings where data scarcity and domain variations are common. To address these issues, we propose MMAL-CL, a unified deep learning framework that integrates an Edge Feature Module (EFM) with multi-subspace mapping attention and an Adaptive Deep Learning Module (ADLM) for cross-domain feature decoupling. The EFM extracts translation-invariant features through residual convolution blocks and a novel multi-subspace attention mechanism, enhancing the model’s ability to capture interdependencies between features. The ADLM enables few-shot learning by mixing task-irrelevant auxiliary data with target domain samples and optimizing feature separation via a dual-classifier strategy. Finally, we evaluated the model’s performance on five datasets (two industrial and three medical) demonstrate that MMAL-CL achieves 99.7% precision on the NEU-CLS dataset with full data and maintains 71.3% precision with only 20 samples per class, outperforming other methods in few-shot settings. The framework shows remarkable cross-domain generalization capability, with an average 12.8% improvement in F1-score over existing methods. These results highlight MMAL-CL’s potential as a practical solution for image detection that can operate effectively with limited training data while maintaining high accuracy across diverse application scenarios.