Semantic Segmentation Improvement Method Based on Deep Supervision for the Construction of Latent Space
The existing convolution operations cannot effectively capture the relationships between long-distance regions in semantic segmentation tasks,resulting in segmentation results that do not conform to human common sense.Accordingly,a semantic segmentation improvement method based on deep supervised latent space construction is proposed.This article adopts the"feature map-hidden space-feature map"process to convert pixel features in an image space into node features in a hidden space,and convert the position and semantic relationships between regions into connection weights between nodes,thereby achieving feature conversion from the feature map to the hidden space.In the process of constructing the hidden space,the Kullback-Leibler divergence loss function is used to supervise the projection matrix,to avoid losing features during the transformation process from feature maps to hidden space nodes.It uses Information Noise Contrastive Estimation(InfoNCE)loss function to supervise node feature and real label representations,ensuring consistency between image features and labels.The proposed method uses Graph Neural Network(GNN)for semantic inference on the constructed latent space,learning the relationships between nodes and endowing the model with the ability to learn semantic relationships between regions,thereby improving the anti-common sense phenomenon in segmentation results.The experimental results on the publicly available dataset CityScapes demonstrate that compared to the baseline segmentation network,the mean Intersection over Union(mIoU)of the proposed method is 81.1%,which is 2.6 percentage points higher than that of the baseline segmentation network and can effectively improve the segmentation results.
semantic segmentationConvolutional Neural Network(CNN)deep supervisionGraph Neural Network(GNN)anti-common sense phenomenon
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