  Training a Fully Convolutional Network (FCN) for semantic segmentation
requires a large number of masks with pixel level labelling, which involves a
large amount of human labour and time for annotation. In contrast, web images
and their image-level labels are much easier and cheaper to obtain. In this
work, we propose a novel method for weakly supervised semantic segmentation
with only image-level labels. The method utilizes the internet to retrieve a
large number of images and uses a large scale co-segmentation framework to
generate masks for the retrieved images. We first retrieve images from search
engines, e.g. Flickr and Google, using semantic class names as queries, e.g.
class names in the dataset PASCAL VOC 2012. We then use high quality masks
produced by co-segmentation on the retrieved images as well as the target
dataset images with image level labels to train segmentation networks. We
obtain an IoU score of 56.9 on test set of PASCAL VOC 2012, which reaches the
state-of-the-art performance.
