Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation

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 \emph{background incompleteness} and \emph{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 \textbf{W}eakly-\textbf{S}upervised \textbf{F}eature \textbf{C}oupling \textbf{N}etwork (\textbf{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, \textbf{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 \textbf{WS-FCN}, which can achieve state-of-the-art results by $65.02\%$ and $64.22\%$ mIoU on PASCAL VOC 2012 \emph{val} set and \emph{test} set, $34.12\%$ mIoU on MS COCO 2014 \emph{val} set, respectively. The code and weight have been released at:~\href{https://github.com/ChunyanWang1/ws-fcn}{WS-FCN}.