  Learning semantic segmentation models under image-level supervision is far
more challenging than under fully supervised setting. Without knowing the exact
pixel-label correspondence, most weakly-supervised methods rely on external
models to infer pseudo pixel-level labels for training semantic segmentation
models. In this paper, we aim to develop a single neural network without
resorting to any external models. We propose a novel self-guided strategy to
fully utilize features learned across multiple levels to progressively generate
the dense pseudo labels. First, we use high-level features as class-specific
localization maps to roughly locate the classes. Next, we propose an
affinity-guided method to encourage each localization map to be consistent with
their intermediate level features. Third, we adopt the training image itself as
guidance and propose a self-guided refinement to further transfer the image's
inherent structure into the maps. Finally, we derive pseudo pixel-level labels
from these localization maps and use the pseudo labels as ground truth to train
the semantic segmentation model. Our proposed self-guided strategy is a unified
framework, which is built on a single network and alternatively updates the
feature representation and refines localization maps during the training
procedure. Experimental results on PASCAL VOC 2012 segmentation benchmark
demonstrate that our method outperforms other weakly-supervised methods under
the same setting.
