A SIMILARITY DISTILLATION GUIDED FEATURE REFINEMENT NETWORK FOR FEW-SHOT SEMANTIC SEGMENTATION

Few-shot semantic segmentation is a challenging task of predicting object categories in pixel-wise with only few annotated samples. Existing methods mainly have two problems, which are representation inconsistency of query and support images and semantic-level feature insufficient. To tackle the two problems, we propose a similarity distillation guided feature refinement network (SD-FRNet). Specifically, we first use support label to generate support similarity feature map and coarse prediction of query image. Then, we use this coarse prediction to generate query similarity feature. To compensate feature representation inconsistency, we conduct knowledge distillation mechanism to align similarity features of query and support images. To enrich semantic-level feature, we further design a feature refinement module, which achieves high-quality segmentation. Extensive experiments show the effectiveness of SD-FRNet. On benchmark datasets, PASCAL-5(i) and COCO-20(i), our proposed SD-FRNet outperform the previous SOTA (state-of-the-art) results.