Weakly supervised semantic segmentation (WSSS) employing weak forms of labels
has been actively studied to alleviate the annotation cost of acquiring
pixel-level labels. However, classifiers trained on biased datasets tend to
exploit shortcut features and make predictions based on spurious correlations
between certain backgrounds and objects, leading to a poor generalization
performance. In this paper, we propose shortcut mitigating augmentation (SMA)
for WSSS, which generates synthetic representations of object-background
combinations not seen in the training data to reduce the use of shortcut
features. Our approach disentangles the object-relevant and background
features. We then shuffle and combine the disentangled representations to
create synthetic features of diverse object-background combinations.
SMA-trained classifier depends less on contexts and focuses more on the target
object when making predictions. In addition, we analyzed the behavior of the
classifier on shortcut usage after applying our augmentation using an
attribution method-based metric. The proposed method achieved the improved
performance of semantic segmentation result on PASCAL VOC 2012 and MS COCO 2014
datasets.

本文提出了用于弱监督语义分割的快捷方式缓解增强方法（SMA），通过生成在训练数据中未见的对象 - 背景组合的合成表示来减少快捷方式特征的使用，在 PASCAL VOC 2012 和 MS COCO 2014 数据集上实现了语义分割结果的改进性能。