We present a causal inference framework to improve Weakly-Supervised Semantic
Segmentation (WSSS). Specifically, we aim to generate better pixel-level
pseudo-masks by using only image-level labels -- the most crucial step in WSSS.
We attribute the cause of the ambiguous boundaries of pseudo-masks to the
confounding context, e.g., the correct image-level classification of "horse"
and "person" may be not only due to the recognition of each instance, but also
their co-occurrence context, making the model inspection (e.g., CAM) hard to
distinguish between the boundaries. Inspired by this, we propose a structural
causal model to analyze the causalities among images, contexts, and class
labels. Based on it, we develop a new method: Context Adjustment (CONTA), to
remove the confounding bias in image-level classification and thus provide
better pseudo-masks as ground-truth for the subsequent segmentation model. On
PASCAL VOC 2012 and MS-COCO, we show that CONTA boosts various popular WSSS
methods to new state-of-the-arts.

本文提出了一种因果推断框架，结合结构因果模型和上下文调整方法以纠正语义分割中的误导性上下文因素，使用图像级标签生成更好的像素级伪掩模，并将其用作后续分割模型的更好伪造地面实况。