Recent years have seen a rapid growth in new approaches improving the
accuracy of semantic segmentation in a weakly supervised setting, i.e. with
only image-level labels available for training. However, this has come at the
cost of increased model complexity and sophisticated multi-stage training
procedures. This is in contrast to earlier work that used only a single stage
$-$ training one segmentation network on image labels $-$ which was abandoned
due to inferior segmentation accuracy. In this work, we first define three
desirable properties of a weakly supervised method: local consistency, semantic
fidelity, and completeness. Using these properties as guidelines, we then
develop a segmentation-based network model and a self-supervised training
scheme to train for semantic masks from image-level annotations in a single
stage. We show that despite its simplicity, our method achieves results that
are competitive with significantly more complex pipelines, substantially
outperforming earlier single-stage methods.

本文探讨了使用弱监督方法进行语义分割的可行性，提出了一种基于单阶段自我监督学习的网络模型，使用图像级注释训练语义掩码取得了与复杂流水线相竞争的结果，优于早期的单阶段方法。

图像标签的单阶段语义分割

Single-Stage Semantic Segmentation from Image Labels

Existing pose estimation approaches fall into two categories: single-stage
and multi-stage methods. While multi-stage methods are seemingly more suited
for the task, their performance in current practice is not as good as
single-stage methods. This work studies this issue. We argue that the current
multi-stage methods' unsatisfactory performance comes from the insufficiency in
various design choices. We propose several improvements, including the
single-stage module design, cross stage feature aggregation, and coarse-to-fine
supervision. The resulting method establishes the new state-of-the-art on both
MS COCO and MPII Human Pose dataset, justifying the effectiveness of a
multi-stage architecture. The source code is publicly available for further
research.

研究单阶段方法与多阶段方法在姿态估计领域的性能表现，提出了单阶段模块设计、跨阶段特征聚合和粗到细的监督等改进建议，所得方法在 MS COCO 和 MPII Human Pose 数据集上都取得了新的最优表现，证明了多阶段架构的有效性。