Dual-Expert Distillation Network for Few-Shot Segmentation

Few-shot segmentation has attracted growing interest owing to its value in practical applications. The primary challenge of few-shot segmentation lies in semantic information discovery, especially for query images. To tackle this issue, we propose a dual-expert distillation network (DEDN) made up of a scenario-level expert and an object-level expert to obtain semantic information from different perspectives. In DEDN, experts can learn from each other through online knowledge distillation with positive-guided Kullback-Leibler divergence. We innovate the Scenario Normalization and Object Continuity Guidance on dual experts to guarantee the various perspectives respectively. We further propose the Adaptive Weighted Fusion to adapt the trained experts to novel classes and obtain reliable fused predictions. Extensive experiments on Pascal-5i and COCO-20i show that our approach achieves state-of-the-art results.