Few-shot Semantic Segmentation addresses the challenge of segmenting objects in query images with only a handful of annotated examples. However, many previous state-of-the-art methods either have to discard intricate local semantic features or suffer from high computational complexity. To address these challenges, we propose a new Few-shot Semantic Segmentation framework based on the transformer architecture. Our approach introduces the spatial transformer decoder and the contextual mask generation module to improve the relational understanding between support and query images. Moreover, we introduce a multi-scale decoder to refine the segmentation mask by incorporating features from different resolutions in a hierarchical manner. Additionally, our approach integrates global features from intermediate encoder stages to improve contextual understanding, while maintaining a lightweight structure to reduce complexity. This balance between performance and efficiency enables our method to achieve state-of-the-art results on benchmark datasets such as $PASCAL-5^i$ and $COCO-20^i$ in both 1-shot and 5-shot settings. Notably, our model with only 1.5 million parameters demonstrates competitive performance while overcoming limitations of existing methodologies. https://github.com/amirrezafateh/MSDNet

本研究解决了在仅有少量标注样本下进行少样本语义分割时的局部语义特征丢失和计算复杂度高的问题。提出了一种基于变换器架构的新框架，利用空间变换解码器和上下文掩码生成模块来增强支持图像与查询图像之间的关系理解，同时通过多尺度解码器优化分割掩码。研究表明，该方法在分析PASCAL-5^i和COCO-20^i基准数据集时显示出卓越的性能和效率平衡。 

MSDNet：基于变换器引导原型的少样本语义分割多尺度解码器