This paper introduces a new convolutional network module for dense prediction tasks, specifically designed for semantic segmentation. The module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The key idea is that dilated convolutions allow for exponential expansion of the receptive field without loss of resolution or coverage. This enables the network to capture multi-scale contextual information while maintaining high-resolution output. The proposed context module is designed to be integrated into existing architectures at any resolution. It is a rectangular prism of convolutional layers with no pooling or subsampling. The module is based on dilated convolutions, which support exponential expansion of the receptive field without loss of resolution or coverage. The module can be plugged into existing architectures to increase their accuracy for dense prediction tasks. The paper also examines the performance of repurposed image classification networks on semantic segmentation. It shows that simplifying the adapted network can increase accuracy. The authors evaluate their context network on the Pascal VOC 2012 dataset and demonstrate that plugging the context module into existing semantic segmentation architectures reliably increases their accuracy. The paper also presents a detailed analysis of dilated convolutions and their application in semantic segmentation. It shows that dilated convolutions can be used to create a new convolutional network architecture that systematically uses dilated convolutions for multi-scale context aggregation. The architecture is motivated by the fact that dilated convolutions support exponentially expanding receptive fields without losing resolution or coverage. The paper also presents a detailed evaluation of the proposed context module on three different datasets for urban scene understanding: CamVid, KITTI, and Cityscapes. The results show that the proposed context module significantly improves the accuracy of semantic segmentation on these datasets. The authors also compare their results to prior work and show that their model outperforms existing methods. The results demonstrate that the proposed context module is both simpler and more accurate than prior models.This paper introduces a new convolutional network module for dense prediction tasks, specifically designed for semantic segmentation. The module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The key idea is that dilated convolutions allow for exponential expansion of the receptive field without loss of resolution or coverage. This enables the network to capture multi-scale contextual information while maintaining high-resolution output. The proposed context module is designed to be integrated into existing architectures at any resolution. It is a rectangular prism of convolutional layers with no pooling or subsampling. The module is based on dilated convolutions, which support exponential expansion of the receptive field without loss of resolution or coverage. The module can be plugged into existing architectures to increase their accuracy for dense prediction tasks. The paper also examines the performance of repurposed image classification networks on semantic segmentation. It shows that simplifying the adapted network can increase accuracy. The authors evaluate their context network on the Pascal VOC 2012 dataset and demonstrate that plugging the context module into existing semantic segmentation architectures reliably increases their accuracy. The paper also presents a detailed analysis of dilated convolutions and their application in semantic segmentation. It shows that dilated convolutions can be used to create a new convolutional network architecture that systematically uses dilated convolutions for multi-scale context aggregation. The architecture is motivated by the fact that dilated convolutions support exponentially expanding receptive fields without losing resolution or coverage. The paper also presents a detailed evaluation of the proposed context module on three different datasets for urban scene understanding: CamVid, KITTI, and Cityscapes. The results show that the proposed context module significantly improves the accuracy of semantic segmentation on these datasets. The authors also compare their results to prior work and show that their model outperforms existing methods. The results demonstrate that the proposed context module is both simpler and more accurate than prior models.