Boosting Few-shot Semantic Segmentation with Transformers

Understand

Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentation has attracted lots of research attention.

• Previous arts extract features from support and query images, which are processed jointly before making predictions on query images.
• The whole process is based on convolutional neural networks (CNN), leading to the problem that only local information is used.
• In this paper, we propose a TRansformer-based Few-shot Semantic segmentation method (TRFS).