Weakly-supervised semantic segmentation (WSSS) with image-level labels has been widely studied to relieve the annotation burden of the traditional segmentation task. In this paper, we show that existing fully-annotated base categories can help segment objects of novel categories with only image-level labels, even if base and novel categories have no overlap. We refer to this task as weak-shot semantic segmentation, which could also be treated as WSSS with auxiliary fully-annotated categories. Recent advanced WSSS methods usually obtain class activation maps (CAMs) and refine them by affinity propagation. Based on the observation that semantic affinity and boundary are class-agnostic, we propose a method under the WSSS framework to transfer semantic affinity and boundary from base categories to novel ones. As a result, we find that pixel-level annotation of base categories can facilitate affinity learning and propagation, leading to higher-quality CAMs of novel categories. Extensive experiments on PASCAL VOC 2012 dataset demonstrate that our method significantly outperforms WSSS baselines on novel categories.

本文提出了一种弱参考语义分割方法，通过在基础类别上添加像素级别的注释，来帮助在只有图像级别标签的情况下，分割新类别对象，实验证明此方法在 PASCAL VOC 2012 数据集上表现显著优于传统方法。

通过转移语义亲和力和边界实现弱监督语义分割