The performance of supervised semantic segmentation methods highly relies on
the availability of large-scale training data. To alleviate this dependence,
few-shot semantic segmentation (FSS) is introduced to leverage the model
trained on base classes with sufficient data into the segmentation of novel
classes with few data. FSS methods face the challenge of model generalization
on novel classes due to the distribution shift between base and novel classes.
To overcome this issue, we propose a class-shared memory (CSM) module
consisting of a set of learnable memory vectors. These memory vectors learn
elemental object patterns from base classes during training whilst re-encoding
query features during both training and inference, thereby improving the
distribution alignment between base and novel classes. Furthermore, to cope
with the performance degradation resulting from the intra-class variance across
images, we introduce an uncertainty-based feature augmentation (UFA) module to
produce diverse query features during training for improving the model's
robustness. We integrate CSM and UFA into representative FSS works, with
experimental results on the widely-used PASCAL-5$^i$ and COCO-20$^i$ datasets
demonstrating the superior performance of ours over state of the art.

我们提出了一种基于少样本语义分割的方法，利用具有充足数据的基础类别训练的模型进行对只有少量数据的新类别的分割，并引入了类别共享记忆模块和基于不确定性的特征增强模块来提高模型的泛化性能。实验结果表明，我们的方法在广泛使用的 PASCAL-5i 和 COCO-20i 数据集上相比最先进的方法具有卓越的性能。