Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data
This addresses the challenge of insufficient labeled data for novel classes in segmentation, offering a domain-specific improvement.
The paper tackles the problem of few-shot segmentation by leveraging unlabeled data to improve model generalization, achieving state-of-the-art performance on PASCAL-5^i and COCO-20^i datasets.
Few-shot segmentation (FSS) aims to train a model which can segment the object from novel classes with a few labeled samples. The insufficient generalization ability of models leads to unsatisfactory performance when the models lack enough labeled data from the novel classes. Considering that there are abundant unlabeled data available, it is promising to improve the generalization ability by exploiting these various data. For leveraging unlabeled data, we propose a novel method, named Image to Pseudo-Episode (IPE), to generate pseudo-episodes from unlabeled data. Specifically, our method contains two modules, i.e., the pseudo-label generation module and the episode generation module. The former module generates pseudo-labels from unlabeled images by the spectral clustering algorithm, and the latter module generates pseudo-episodes from pseudo-labeled images by data augmentation methods. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ demonstrate that our method achieves the state-of-the-art performance for FSS.