  Weakly supervised semantic segmentation (WSSS), which aims to mine the object
regions by merely using class-level labels, is a challenging task in computer
vision. The current state-of-the-art CNN-based methods usually adopt
Class-Activation-Maps (CAMs) to highlight the potential areas of the object,
however, they may suffer from the part-activated issues. To this end, we try an
early attempt to explore the global feature attention mechanism of vision
transformer in WSSS task. However, since the transformer lacks the inductive
bias as in CNN models, it can not boost the performance directly and may yield
the over-activated problems. To tackle these drawbacks, we propose a
Convolutional Neural Networks Refined Transformer (CRT) to mine a globally
complete and locally accurate class activation maps in this paper. To validate
the effectiveness of our proposed method, extensive experiments are conducted
on PASCAL VOC 2012 and CUB-200-2011 datasets. Experimental evaluations show
that our proposed CRT achieves the new state-of-the-art performance on both the
weakly supervised semantic segmentation task the weakly supervised object
localization task, which outperform others by a large margin.
