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The initial seed based on the convolutional neural network (CNN) for weaklysupervised semantic segmentation always highlights the most discriminativeregions but fails to identify the global target information. Methods based ontransformers have been proposed successively benefiting from the advantage ofcapturing long-range feature representations. However, we observe a flawregardless of the gifts based on the transformer. Given a class, the initialseeds generated based on the transformer may invade regions belonging to otherclasses. Inspired by the mentioned issues, we devise a simple yet effectivemethod with Multi-estimations Complementary Patch (MECP) strategy and AdaptiveConflict Module (ACM), dubbed MECPformer. Given an image, we manipulate it withthe MECP strategy at different epochs, and the network mines and deeply fusesthe semantic information at different levels. In addition, ACM adaptivelyremoves conflicting pixels and exploits the network self-training capability tomine potential target information. Without bells and whistles, our MECPformerhas reached new state-of-the-art 72.0% mIoU on the PASCAL VOC 2012 and 42.4% onMS COCO 2014 dataset. The code is available athttps://github.com/ChunmengLiu1/MECPformer.

MECPformer: Multi-estimations Complementary Patch with CNN-Transformers for Weakly Supervised Semantic Segmentation