  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.
