Hierarchical context-agnostic network with contrastive feature diversity for one-shot semantic segmentation

One-shot semantic segmentation aims at distinguishing pixels of an unseen category from the background, using merely one annotated image from the same category. However, most previous works neglect the feature diversity of foreground and the context information of background by using the masked average pooling. To solve these issues, we propose the Hierarchical Context-Agnostic Network (HCNet). It mainly includes two modules: (1) a Hierarchical Pyramid Supportive (HPS) module that generate the hierarchical supportive prototypes from coarse to fine to ensure feature diversity, and (2) a Background Exclusion Supportive (BES) module that explicitly introduces the contrastive information from the background for more precise category features. We conduct extensive experiments on Pascal-5ⁱ and COCO-20ⁱ to evaluate the performance of HCNet. HCNet achieves the mIoU score of 62.1% on Pascal-5ⁱ and 40.7% on COCO-20ⁱ and outperforms other works for the challenging one-shot segmentation, which has proved the efficiency of the whole network. Code is available at https://github.com/fangzy97/hcnet.