  Image-level weakly supervised semantic segmentation has received increasing
attention due to its low annotation cost. Existing methods mainly rely on Class
Activation Mapping (CAM) to obtain pseudo-labels for training semantic
segmentation models. In this work, we are the first to demonstrate that
long-tailed distribution in training data can cause the CAM calculated through
classifier weights over-activated for head classes and under-activated for tail
classes due to the shared features among head- and tail- classes. This degrades
pseudo-label quality and further influences final semantic segmentation
performance. To address this issue, we propose a Shared Feature Calibration
(SFC) method for CAM generation. Specifically, we leverage the class prototypes
that carry positive shared features and propose a Multi-Scaled
Distribution-Weighted (MSDW) consistency loss for narrowing the gap between the
CAMs generated through classifier weights and class prototypes during training.
The MSDW loss counterbalances over-activation and under-activation by
calibrating the shared features in head-/tail-class classifier weights.
Experimental results show that our SFC significantly improves CAM boundaries
and achieves new state-of-the-art performances. The project is available at
https://github.com/Barrett-python/SFC.
