  Thanks to the advantages of the friendly annotations and the satisfactory
performance, Weakly-Supervised Semantic Segmentation (WSSS) approaches have
been extensively studied. Recently, the single-stage WSSS was awakened to
alleviate problems of the expensive computational costs and the complicated
training procedures in multi-stage WSSS. However, results of such an immature
model suffer from problems of background incompleteness and object
incompleteness. We empirically find that they are caused by the insufficiency
of the global object context and the lack of the local regional contents,
respectively. Under these observations, we propose a single-stage WSSS model
with only the image-level class label supervisions, termed as Weakly Supervised
Feature Coupling Network (WS-FCN), which can capture the multi-scale context
formed from the adjacent feature grids, and encode the fine-grained spatial
information from the low-level features into the high-level ones. Specifically,
a flexible context aggregation module is proposed to capture the global object
context in different granular spaces. Besides, a semantically consistent
feature fusion module is proposed in a bottom-up parameter-learnable fashion to
aggregate the fine-grained local contents. Based on these two modules, WS-FCN
lies in a self-supervised end-to-end training fashion. Extensive experimental
results on the challenging PASCAL VOC 2012 and MS COCO 2014 demonstrate the
effectiveness and efficiency of WS-FCN, which can achieve state-of-the-art
results by 65.02\% and 64.22\% mIoU on PASCAL VOC 2012 val set and test set,
34.12\% mIoU on MS COCO 2014 val set, respectively. The code and weight have
been released at:https://github.com/ChunyanWang1/ws-fcn.
