Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples. However, this feature sharing mechanism inevitably causes semantic aliasing between novel classes when they have similar compositions of semantic concepts. In this paper, we reformulate few-shot segmentation as a semantic reconstruction problem, and convert base class features into a series of basis vectors which span a class-level semantic space for novel class reconstruction. By introducing contrastive loss, we maximize the orthogonality of basis vectors while minimizing semantic aliasing between classes. Within the reconstructed representation space, we further suppress interference from other classes by projecting query features to the support vector for precise semantic activation. Our proposed approach, referred to as anti-aliasing semantic reconstruction (ASR), provides a systematic yet interpretable solution for few-shot learning problems. Extensive experiments on PASCAL VOC and MS COCO datasets show that ASR achieves strong results compared with the prior works.

本文提出了反混叠语义重建（ASR）方法，以解决在已学习过的基础类上表示新型类别时出现的语义歧义问题，该方法通过引入基础向量并最大化其正交性来实现对类别的重建，同时通过对查询特征进行投影来抑制来自其他类别的干扰信号。在PASCAL VOC和MS COCO数据集上的实验结果表明，ASR方法相对于之前的方法取得了较好的效果。

针对少样本语义分割的抗锯齿语义重建