  Weakly supervised semantic segmentation (WSSS) based on image-level labels is
challenging since it is hard to obtain complete semantic regions. To address
this issue, we propose a self-training method that utilizes fused multi-scale
class-aware attention maps. Our observation is that attention maps of different
scales contain rich complementary information, especially for large and small
objects. Therefore, we collect information from attention maps of different
scales and obtain multi-scale attention maps. We then apply denoising and
reactivation strategies to enhance the potential regions and reduce noisy
areas. Finally, we use the refined attention maps to retrain the network.
Experiments showthat our method enables the model to extract rich semantic
information from multi-scale images and achieves 72.4% mIou scores on both the
PASCAL VOC 2012 validation and test sets. The code is available at
https://bupt-ai-cz.github.io/SMAF.

Computer Vision and Pattern Recognition