CVPR 2023 - Hierarchical Dense Correlation Distillation for Few-Shot Segmentation

In this episode we discuss Hierarchical Dense Correlation Distillation for Few-Shot Segmentation by Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chenyao Wang, Shu Liu, Jingyong Su, Jiaya Jia. The paper proposes a Hierarchically Decoupled Matching Network (HDMNet) for few-shot semantic segmentation (FSS), where a class-agnostic model segments unseen classes with only a few annotations. The method focuses on mining pixel-level support correlation based on the transformer architecture, and uses self-attention modules for establishing hierarchical dense features for cascade matching between query and support features. The proposed matching module reduces train-set overfitting and introduces correlation distillation leveraging semantic correspondence from coarse to fine resolution, resulting in decent performance on COCO-20i dataset, achieving 50% mIoU on one-shot and 56% on five-shot segmentation. Code is available on the project website.