Class Similarity Transition: Decoupling Class Similarities and Imbalance
  from Generalized Few-shot Segmentation
Shihong Wang Ruixun Liu Kaiyu Li Jiawei Jiang Xiangyong Cao
In Generalized Few-shot Segmentation (GFSS), a model is trained with a large
corpus of base class samples and then adapted on limited samples of novel
classes. This paper focuses on the relevance between base and novel classes,
and improves GFSS in two aspects: 1) mining the similarity between base and
novel classes to promote the learning of novel classes, and 2) mitigating the
class imbalance issue caused by the volume difference between the support set
and the training set. Specifically, we first propose a similarity transition
matrix to guide the learning of novel classes with base class knowledge. Then,
we leverage the Label-Distribution-Aware Margin (LDAM) loss and Transductive
Inference to the GFSS task to address the problem of class imbalance as well as
overfitting the support set. In addition, by extending the probability
transition matrix, the proposed method can mitigate the catastrophic forgetting
of base classes when learning novel classes. With a simple training phase, our
proposed method can be applied to any segmentation network trained on base
classes. We validated our methods on the adapted version of OpenEarthMap.
Compared to existing GFSS baselines, our method excels them all from 3% to 7%
and ranks second in the OpenEarthMap Land Cover Mapping Few-Shot Challenge at
the completion of this paper. Code:
https://github.com/earth-insights/ClassTrans