  Weakly-supervised semantic segmentation aims to assign each pixel a semantic
category under weak supervisions, such as image-level tags. Most of existing
weakly-supervised semantic segmentation methods do not use any feedback from
segmentation output and can be considered as open-loop systems. They are prone
to accumulated errors because of the static seeds and the sensitive structure
information. In this paper, we propose a generic self-adaptation mechanism for
existing weakly-supervised semantic segmentation methods by introducing two
feedback chains, thus constituting a closed-loop system. Specifically, the
first chain iteratively produces dynamic seeds by incorporating cross-image
structure information, whereas the second chain further expands seed regions by
a customized random walk process to reconcile inner-image structure information
characterized by superpixels. Experiments on PASCAL VOC 2012 suggest that our
network outperforms state-of-the-art methods with significantly less
computational and memory burden.
