Learning graph structures with transformer for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) is a challenging task of computer vision. The state-of-the-art semantic segmentation methods are usually based on the convolutional neural network (CNN), which mainly have the drawbacks of inability to explore the global information correctly and failure to activate potential object regions. To avoid such drawbacks, the transformer approach is explored in the WSSS task, but no effective semantic association between different patch tokens can be determined in the transformer. To address this issue, inspired by the graph convolutional network (GCN), this paper proposes a graph structure to learn the semantic category relationships between different blocks in the vector sequence. To verify the effectiveness of the proposed method in this paper, a large number of experiments were conducted on the publicly available PASCAL VOC2012 dataset. The experimental results show that our proposed method achieves significant performance improvement in the WSSS task and outperforms other state-of-the-art transformer-based methods.