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.

翻译：增量式小样本语义分割（IFSS）旨在仅利用少量像素级标注数据，逐步将语义分割模型扩展到新类别，同时保持其对已学习基类别的分割能力。由于数据不平衡，该任务面临基类与新类别之间的严重语义混淆问题，导致分割结果不理想。为缓解此问题，我们提出语义引导的关系对齐与自适应（SRAA）方法，该方法充分考虑先验语义信息的引导作用。具体而言，我们首先在基类学习阶段执行语义关系对齐（SRA），将基类表示与其语义进行语义对齐。由此，基类嵌入被约束为与不同类别之间的语义相关性较低。随后，在增量学习阶段，基于已语义对齐的基类，采用语义引导自适应（SGA）方法，旨在确保所遇新类别的视觉嵌入与语义嵌入之间的亲和性，从而使特征表示与其语义信息保持一致。通过这种方式，可有效抑制语义混淆问题。我们在PASCAL VOC 2012和COCO数据集上评估了模型。两个数据集上的实验结果表明该模型具有竞争性性能，证明了我们方法的优越性。