Decoupling Class Similarities and Imbalance to Improve Generalized Few-shot Semantic Segmentation

The core message of this paper is to address the relevance between base and novel classes, and the class imbalance issue in the Generalized Few-shot Semantic Segmentation (GFSS) task. The authors propose a similarity transition matrix to guide the learning of novel classes with base class knowledge, and leverage the Label-Distribution-Aware Margin (LDAM) loss and Transductive Inference to mitigate the class imbalance problem.