State-of-the-art approaches for semantic segmentation rely on deep
convolutional neural networks trained on fully annotated datasets, that have
been shown to be notoriously expensive to collect, both in terms of time and
money. To remedy this situation, weakly supervised methods leverage other forms
of supervision that require substantially less annotation effort, but they
typically present an inability to predict precise object boundaries due to
approximate nature of the supervisory signals in those regions. While great
progress has been made in improving the performance, many of these weakly
supervised methods are highly tailored to their own specific settings. This
raises challenges in reusing algorithms and making steady progress. In this
paper, we intentionally avoid such practices when tackling weakly supervised
semantic segmentation. In particular, we train standard neural networks with
partial cross-entropy loss function for the labeled pixels and our proposed
Gated CRF loss for the unlabeled pixels. The Gated CRF loss is designed to
deliver several important assets: 1) it enables flexibility in the kernel
construction to mask out influence from undesired pixel positions; 2) it
offloads learning contextual relations to CNN and concentrates on semantic
boundaries; 3) it does not rely on high-dimensional filtering and thus has a
simple implementation. Throughout the paper we present the advantages of the
loss function, analyze several aspects of weakly supervised training, and show
that our `purist' approach achieves state-of-the-art performance for both
click-based and scribble-based annotations.

该论文介绍了一种新的弱监督方法，将神经网络与部分交叉熵损失函数以及门控条件随机场损失函数相结合，以达到在标注像素点与未标注像素点上达到最优的分割效果。