Research on Multi-task Semantic Segmentation Based on Attention and Feature Fusion Method

Recently, single-task learning on semantic segmentation tasks has achieved good results. When multiple tasks are handled simultaneously, single-task learning requires an independent network structure for each task and no intersection between tasks. This paper proposes a feature fusion-attention mechanism multi-task learning method, which can simultaneously handle multiple related tasks (semantic segmentation, surface normal estimation task, etc.). Our model includes a feature extraction module to extract semantic information at different scales, a feature fusion module to refine the extracted features, and an attentional mechanism for processing information from fusion modules to learn information about specific tasks. The network architecture proposed in this paper trains in an end-to-end manner and simultaneously improves the performance of multiple tasks. Experiments are carried out on two well-known semantic segmentation datasets, and the accuracy of the proposed model is verified.