The conventional approach to semantic segmentation necessitates training models on extensive datasets, a process that is often resource-intensive and time-consuming. Few-shot learning methods, by contrast, employ previously trained models to rapidly adapt to novel, unseen classes. These methods utilize a limited set of k samples to establish prototypes representing the novel class, guiding the model’s predictions and facilitating iterative weight adjustments in alignment with this foundational structure. In this study, we build on these strengths, augmenting the proposed system with multiple neural architectures incorporating attention modules. Specifically, we employ a 1-shot learning strategy across k different models (with \(k=3\) in our experiments), whose aggregated results enable a comprehensive representation of the novel class’s features with minimal data support. The conducted experiments have shown that a properly selected consensus method can have a positive impact on the obtained segmentation results.