STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation

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…

• NN
  National Natural Science Foundation of China

  Awards: 61402268, 61572264, 61532005, 61210006
• NK
  National Key Research and Development Program of China

  Award: 2016YFB0800404