Lightweight Segformer Semantic Segmentation Based on Cascaded Attention and Boundary Prediction Improvements
To solve the problem that multi-scale information cannot be effectively aggregated and used and the boundaries are blurred in Segformer network,a semantic segmentation network based on cascaded attention and boundary prediction is proposed.In the decoder part of Segformer,by using cascaded attention,multi-scale feature information is effectively aggregated and boundary prediction is performed by aggregating multi-scale features to provide assistance for semantic segmentation tasks.In the gradient update part,gradient surgery is added to reduce the interference to training by gradient conflicts between tasks caused by adding auxiliary tasks and speed up model convergence.Experiments are conducted on ADE20k dataset and Cityscapes dataset.By increasing the calculation amount of 2.69 M parameters and 24.67 G,the average intersection over union ratio of the network is improved by 2.38％,proving the effectiveness of the proposed method.
Segformercascaded attentionauxiliary tasksboundary predictiongradient surgery
万方数据
维普
