This paper proposes an efficient piecewise training method for deep structured models to improve semantic segmentation. The authors introduce a deep structured model that combines convolutional neural networks (CNNs) with conditional random fields (CRFs) to capture semantic correlations between image regions. The model uses CNN-based pairwise potential functions to model semantic relations between neighboring patches and incorporates patch-patch and patch-background contexts. The patch-patch context is modeled using CRFs with CNN-based pairwise potentials to capture semantic correlations between neighboring patches. The patch-background context is modeled using a network design with multi-scale image input and sliding pyramid pooling. The proposed method achieves state-of-the-art performance on several popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow, with an intersection-over-union (IoU) score of 78.0 on the PASCAL VOC 2012 dataset. The method uses efficient piecewise training of the CRF to avoid repeated expensive CRF inference during back propagation. The model is evaluated on multiple datasets and shows significant improvements in performance compared to existing methods. The authors also discuss the benefits of using multi-scale networks and sliding pyramid pooling for encoding rich background information. The method is effective in capturing semantic relations between image regions and improves the final segmentation results through a combination of CNN-based potentials and traditional smoothness potentials. The proposed method is efficient and effective for semantic segmentation tasks.This paper proposes an efficient piecewise training method for deep structured models to improve semantic segmentation. The authors introduce a deep structured model that combines convolutional neural networks (CNNs) with conditional random fields (CRFs) to capture semantic correlations between image regions. The model uses CNN-based pairwise potential functions to model semantic relations between neighboring patches and incorporates patch-patch and patch-background contexts. The patch-patch context is modeled using CRFs with CNN-based pairwise potentials to capture semantic correlations between neighboring patches. The patch-background context is modeled using a network design with multi-scale image input and sliding pyramid pooling. The proposed method achieves state-of-the-art performance on several popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow, with an intersection-over-union (IoU) score of 78.0 on the PASCAL VOC 2012 dataset. The method uses efficient piecewise training of the CRF to avoid repeated expensive CRF inference during back propagation. The model is evaluated on multiple datasets and shows significant improvements in performance compared to existing methods. The authors also discuss the benefits of using multi-scale networks and sliding pyramid pooling for encoding rich background information. The method is effective in capturing semantic relations between image regions and improves the final segmentation results through a combination of CNN-based potentials and traditional smoothness potentials. The proposed method is efficient and effective for semantic segmentation tasks.