This paper presents a fresh perspective on the role of saliency maps in
weakly-supervised semantic segmentation (WSSS) and offers new insights and
research directions based on our empirical findings. We conduct comprehensive
experiments and observe that the quality of the saliency map is a critical
factor in saliency-guided WSSS approaches. Nonetheless, we find that the
saliency maps used in previous works are often arbitrarily chosen, despite
their significant impact on WSSS. Additionally, we observe that the choice of
the threshold, which has received less attention before, is non-trivial in
WSSS. To facilitate more meaningful and rigorous research for saliency-guided
WSSS, we introduce \texttt{WSSS-BED}, a standardized framework for conducting
research under unified conditions. \texttt{WSSS-BED} provides various saliency
maps and activation maps for seven WSSS methods, as well as saliency maps from
unsupervised salient object detection models.

基于对显著性图在弱监督语义分割中的作用的新见解和实证发现，该论文提出了对显著性图的新视角，并展示了其在研究方向上的启示。通过全面的实验，发现显著性图的质量是显著性引导的弱监督语义分割中的关键因素；同时指出之前研究中使用的显著性图往往是任意选择的，尽管它们对于弱监督语义分割具有重要影响；此外，注意到之前对于阈值选择的关注较少，但在弱监督语义分割中具有重要性。为了促进显著性引导的弱监督语义分割的更有意义和严密的研究，介绍了	exttt {WSSS-BED}，这是一个用于在统一条件下进行研究的标准化框架，提供了七种 WSSS 方法的各种显著性图和激活图，以及来自无监督显著目标检测模型的显著性图。