  We propose a novel weakly-supervised semantic segmentation algorithm based on
Deep Convolutional Neural Network (DCNN). Contrary to existing
weakly-supervised approaches, our algorithm exploits auxiliary segmentation
annotations available for different categories to guide segmentations on images
with only image-level class labels. To make the segmentation knowledge
transferrable across categories, we design a decoupled encoder-decoder
architecture with attention model. In this architecture, the model generates
spatial highlights of each category presented in an image using an attention
model, and subsequently generates foreground segmentation for each highlighted
region using decoder. Combining attention model, we show that the decoder
trained with segmentation annotations in different categories can boost the
performance of weakly-supervised semantic segmentation. The proposed algorithm
demonstrates substantially improved performance compared to the
state-of-the-art weakly-supervised techniques in challenging PASCAL VOC 2012
dataset when our model is trained with the annotations in 60 exclusive
categories in Microsoft COCO dataset.
