  The initial seed based on the convolutional neural network (CNN) for weakly
supervised semantic segmentation always highlights the most discriminative
regions but fails to identify the global target information. Methods based on
transformers have been proposed successively benefiting from the advantage of
capturing long-range feature representations. However, we observe a flaw
regardless of the gifts based on the transformer. Given a class, the initial
seeds generated based on the transformer may invade regions belonging to other
classes. Inspired by the mentioned issues, we devise a simple yet effective
method with Multi-estimations Complementary Patch (MECP) strategy and Adaptive
Conflict Module (ACM), dubbed MECPformer. Given an image, we manipulate it with
the MECP strategy at different epochs, and the network mines and deeply fuses
the semantic information at different levels. In addition, ACM adaptively
removes conflicting pixels and exploits the network self-training capability to
mine potential target information. Without bells and whistles, our MECPformer
has reached new state-of-the-art 72.0% mIoU on the PASCAL VOC 2012 and 42.4% on
MS COCO 2014 dataset. The code is available at
https://github.com/ChunmengLiu1/MECPformer.
