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}.

翻译：鉴于友好标注与满意性能的优势，弱监督语义分割（WSSS）方法已被广泛研究。近期，单阶段WSSS方法被唤醒以缓解多阶段WSSS中计算成本高昂与训练流程复杂的问题。然而，这类不成熟模型的结果仍面临"背景不完整"与"目标不完整"的问题。我们通过实验发现，这些问题分别源于全局目标上下文的缺失和局部区域内容的不足。基于这些观察，我们提出仅依赖图像级类别标签监督的单阶段WSSS模型——**弱监督特征耦合网络（WS-FCN）**，该网络能够捕获相邻特征网格形成的多尺度上下文，并将低层特征中的细粒度空间信息编码至高层特征。具体而言，我们提出灵活上下文聚合模块，以在不同粒度空间中捕获全局目标上下文；同时提出语义一致的特征融合模块，采用自底向上参数可学习方式聚合细粒度局部内容。基于上述两个模块，**WS-FCN**采用自监督端到端训练范式。在具有挑战性的PASCAL VOC 2012与MS COCO 2014数据集上的大量实验证明了**WS-FCN**的有效性与高效性：其在PASCAL VOC 2012验证集和测试集上分别达到65.02%与64.22%的mIoU，在MS COCO 2014验证集上达到34.12%的mIoU，均实现当前最优结果。代码与权重已发布于：\href{https://github.com/ChunyanWang1/ws-fcn}{WS-FCN}。