Few-Shot Learning-Based Analysis of Production Areas Using Large Foundation Models and Metric Learning

Digital sensing of production areas and subsequent automated analysis of the captured data can significantly improve the efficiency of factory planning processes. The segmentation of class-specific regions in the captured data using deep neural networks shows great potential for such analysis. However, previous approaches are based on supervised learning and, therefore, require comprehensive, annotated datasets that are costly to generate. The use of large foundation models such as DINOv2 or SAM, combined with few-shot learning approaches, could reduce these efforts in the future. In this work, we first present a method that implements such a combination and subsequently evaluate its performance on an exemplary dataset. The obtained results confirm the method's potential, especially in scenarios with limited availability of labeled data.