The goal of incremental Few-shot Semantic Segmentation (iFSS) is to extend
pre-trained segmentation models to new classes via few annotated images without
access to old training data. During incrementally learning novel classes, the
data distribution of old classes will be destroyed, leading to catastrophic
forgetting. Meanwhile, the novel classes have only few samples, making models
impossible to learn the satisfying representations of novel classes. For the
iFSS problem, we propose a network called OINet, i.e., the background embedding
space \textbf{O}rganization and prototype \textbf{I}nherit Network.
Specifically, when training base classes, OINet uses multiple classification
heads for the background and sets multiple sub-class prototypes to reserve
embedding space for the latent novel classes. During incrementally learning
novel classes, we propose a strategy to select the sub-class prototypes that
best match the current learning novel classes and make the novel classes
inherit the selected prototypes' embedding space. This operation allows the
novel classes to be registered in the embedding space using few samples without
affecting the distribution of the base classes. Results on Pascal-VOC and COCO
show that OINet achieves a new state of the art.

在增量少样本语义分割（iFSS）问题中，我们提出了一种名为 OINet 的网络，即背景嵌入空间组织和原型继承网络，通过在训练基础类别时使用多个分类头和多个子类原型来保留新类别的嵌入空间，并提出了一种策略来选择最匹配当前学习新类别的子类原型，使新类别能够在少样本下注册在嵌入空间中，而不影响基础类别的分布，Pascal-VOC 和 COCO 上的结果表明，OINet 取得了最新的技术成果。

组织背景以探索潜在类别的增量少样本语义分割

Organizing Background to Explore Latent Classes for Incremental Few-shot  Semantic Segmentation

Incremental few-shot semantic segmentation (IFSS) aims to incrementally
extend a semantic segmentation model to novel classes according to only a few
pixel-level annotated data, while preserving its segmentation capability on
previously learned base categories. This task faces a severe semantic-aliasing
issue between base and novel classes due to data imbalance, which makes
segmentation results unsatisfactory. To alleviate this issue, we propose the
Semantic-guided Relation Alignment and Adaptation (SRAA) method that fully
considers the guidance of prior semantic information. Specifically, we first
conduct Semantic Relation Alignment (SRA) in the base step, so as to
semantically align base class representations to their semantics. As a result,
the embeddings of base classes are constrained to have relatively low semantic
correlations to categories that are different from them. Afterwards, based on
the semantically aligned base categories, Semantic-Guided Adaptation (SGA) is
employed during the incremental learning stage. It aims to ensure affinities
between visual and semantic embeddings of encountered novel categories, thereby
making the feature representations be consistent with their semantic
information. In this way, the semantic-aliasing issue can be suppressed. We
evaluate our model on the PASCAL VOC 2012 and the COCO dataset. The
experimental results on both these two datasets exhibit its competitive
performance, which demonstrates the superiority of our method.

本文提出一种基于 Semantic-guided Relation Alignment and Adaptation (SRAA) 方法的增量式 few-shot 语义分割方法，解决了在增量学习后的语义混淆问题，并通过在 PASCAL VOC 2012 和 COCO 数据集上的实验结果表明了它的有效性。