Image segmentation technology is an important branch of visual understanding system, with contributing to promote the accuracy of classification. In recent years, the integration of deep learning and image processing has produced a new generation of image segmentation algorithms. Compared with traditional methods, the performance has been improved and reached high accuracy. This work mainly introduces traditional or based on deep learning and compares the performance of related algorithms. To solve the problems of existing algorithms, the future development trend is prospected.
The Transformer has achieved tremendous success in computer vision, natural language processing, and graph representation learning. However, the transformer cannot effectively encode the topology information of the graph into the model, while it is the advantage of the graph convolution network (GCN). Therefore, we propose a model GTGC combining transformer and GCN for graph classification tasks. To this end, we take the result of graph data passing through multi-head self-attention and feed-forward blocks as the input of the graph convolution module. By increasing the number of neighbors for each node’s feature matrix, the nodes with more neighbors are more important in the attention mechanism. We validate the validity of the model on multiple data sets, including social network datasets and bioinformatics datasets. Experimental results demonstrate that our model achieves advanced accuracy
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