  Deep neural network-based semantic segmentation generally requires
large-scale cost extensive annotations for training to obtain better
performance. To avoid pixel-wise segmentation annotations which are needed for
most methods, recently some researchers attempted to use object-level labels
(e.g. bounding boxes) or image-level labels (e.g. image categories). In this
paper, we propose a novel recursive coarse-to-fine semantic segmentation
framework based on only image-level category labels. For each image, an initial
coarse mask is first generated by a convolutional neural network-based
unsupervised foreground segmentation model and then is enhanced by a graph
model. The enhanced coarse mask is fed to a fully convolutional neural network
to be recursively refined. Unlike existing image-level label-based semantic
segmentation methods which require to label all categories for images contain
multiple types of objects, our framework only needs one label for each image
and can handle images contains multi-category objects. With only trained on
ImageNet, our framework achieves comparable performance on PASCAL VOC dataset
as other image-level label-based state-of-the-arts of semantic segmentation.
Furthermore, our framework can be easily extended to foreground object
segmentation task and achieves comparable performance with the state-of-the-art
supervised methods on the Internet Object dataset.
