Dual-branch aggregation and edge refinement network for few shot semantic segmentation
摘要
Existing Few-Shot Semantic Segmentation (FSS) methods often focus on extracting semantic information from support images to guide the segmentation of query images, while less attention is paid to exploring the query branch. However, due to the limited number of support images, there exists significant intra-class variance between support and query images. Additionally, relying solely on a single support prototype to guide query segmentation often leads to inaccurate segmentation boundaries in the prediction results, which can affect the model’s performance. In this study, we simultaneously consider the information extraction from both the support and query branches and propose a Dual-branch Aggregation and Edge Refinement (DAER) network for accurate query image segmentation. Specifically, to better explore the information from the query branch, we introduce an Initial Mask Generation Module (IMGM) that generates an initial mask for the query image. Furthermore, we propose a Dual-Branch Aggregation Module (DBAM) that simultaneously captures information from both the support and query branches. Finally, an Edge Refinement Module (ERM) is introduced to integrate more query-specific positional information into the network. Extensive experiments on standard few-shot semantic segmentation benchmarks, including PASCAL-