Progressive Training Enabled Fine-Grained Recognition

Few-shot semantic segmentation(FSS) is intended to segment a foreground object from a query image with a novel object using only a few annotated support images. Although attracting the attention of many researchers, this challenging problem remains to be not well solved due to two critical issues: (1)The information mismatching between support and query features leads to model distraction. (2)The key feature of query images is not activated well. in this paper, we introduce the Prior Semantic Harmonization Network(PSHNet) to tackle these limitations. PSHNet is composed of three effective modules. The Semantic Harmonization Module(SHM) corrects the information matching between support and query images, while the Feature Activation Module(FAM) activates the key feature of query images. Furthermore, we introduce a Hierarchical Aggregation Module(HAM) to refine each output of the multi-scale module. Experiments show that our model achieves an excellent performance on both PASCAL-5$^{i}$ and COCO-20$^{i}$ datasets.