Generalized few-shot semantic segmentation (GFSS) aims to segment objects of both base and novel classes, using sufficient samples of base classes and few samples of novel classes. Representative GFSS approaches typically employ a two-phase training scheme, involving base class pre-training followed by novel class fine-tuning, to learn the classifiers for base and novel classes respectively. Nevertheless, distribution gap exists between base and novel classes in this process. To narrow this gap, we exploit effective knowledge transfer from base to novel classes. First, a novel prototype modulation module is designed to modulate novel class prototypes by exploiting the correlations between base and novel classes. Second, a novel classifier calibration module is proposed to calibrate the weight distribution of the novel classifier according to that of the base classifier. Furthermore, existing GFSS approaches suffer from a lack of contextual information for novel classes due to their limited samples, we thereby introduce a context consistency learning scheme to transfer the contextual knowledge from base to novel classes. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ demonstrate that our approach significantly enhances the state of the art in the GFSS setting. The code is available at: https://github.com/HHHHedy/GFSS-EKT.

翻译：广义少样本语义分割（GFSS）旨在利用充足的基础类样本和少量的新类样本，同时分割基础类和新类物体。代表性的GFSS方法通常采用两阶段训练方案，即先进行基础类预训练，再进行新类微调，以分别学习基础类和新类的分类器。然而，这一过程中基础类与新类之间存在分布差异。为缩小该差异，我们探索从基础类到新类的有效知识迁移。首先，设计了一种新颖的原型调制模块，通过利用基础类与新类之间的相关性来调制新类原型。其次，提出了一种新颖的分类器校准模块，根据基础类分类器的权重分布来校准新类分类器的权重分布。此外，由于现有GFSS方法中新类样本有限，缺乏上下文信息，我们因此引入上下文一致性学习方案，将上下文知识从基础类迁移至新类。在PASCAL-5$^i$和COCO-20$^i$上的大量实验表明，我们的方法在GFSS设定下显著提升了当前最优性能。代码发布于：https://github.com/HHHHedy/GFSS-EKT。