Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmentation masks, and annotating these images is very costly in terms of both finance and human effort. In this paper, we propose a simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation. Specifically, we first train an initial segmentation network called Initial-DCNN with the saliency maps of simple images (i.e., those with a single category of major object(s) and clean background). These saliency maps can be automatically obtained by existing bottom-up salient object detection techniques, where no supervision information is needed. Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as well as the image-level annotations. Finally, more pixel-level segmentation masks of complex images (two or more categories of objects with cluttered background), which are inferred by using Enhanced-DCNN and image-level annotations, are utilized as the supervision information to learn the Powerful-DCNN for semantic segmentation. Our method utilizes $40$K simple images from Flickr.com and 10K complex images from PASCAL VOC for step-wisely boosting the segmentation network. Extensive experimental results on PASCAL VOC 2012 segmentation benchmark demonstrate that the proposed STC framework outperforms the state-of-the-art algorithms for weakly-supervised semantic segmentation by a large margin (e.g., 10.6% over MIL-ILP-seg [1]).

本文提出了一种简单有效的框架（STC）方法，在此方法中，只利用基于图像级别注释的简单图像对已训练的深度卷积神经网络（DCNN）进行语义分割。通过本文的方法，可以有效减少由于人工标记带来的显著算力和人力成本，使语义对象分割的效果得到显著提升。

STC: 一种简单到复杂的弱监督语义分割框架