Dual-level correspondence network for few-shot semantic segmentation
摘要
Few-shot semantic segmentation (FSS) aims to segment novel class objects with only a few labeled support images from the same class. Most previous works exploit the prototype learning or affinity learning framework to extract single-level correspondence between support and query sets. However, single-level correspondence from the object or pixel information fails to fully mine semantic correlation, thus leading to incomplete segmentation or background noise. To address this issue, we propose the Dual-Level Correspondence Network (DLCNet) to establish the complementary correspondence with support prototype and pixel information guidance. The dual-level correspondence generation module accomplishes the dense matching between query features and dual-level object information to establish dual-level correspondence. Moreover, we design the attention mask generation module to alleviate the generalization reduction based on the multi-level prior attention and introduce the multi-scale feature adaptive fusion module to boost fusion features and refine fine-grained segmentation. Extensive experiments on PASCAL-