RefineNet is a novel multi-path refinement network designed for high-resolution semantic segmentation. It addresses the limitation of deep CNNs, which often lose fine details during downsampling. By exploiting features at multiple levels of abstraction, RefineNet refines coarse high-level semantic features with fine-grained low-level features using long-range residual connections. This approach allows for effective end-to-end training and enables the network to capture rich background context efficiently through chained residual pooling. The method achieves state-of-the-art performance on seven public datasets, including an intersection-over-union (IoU) score of 83.4 on the challenging PASCAL VOC 2012 dataset, outperforming the best previous method, DeepLab. The paper also discusses related work, provides a detailed description of the network architecture, and presents comprehensive experimental results to demonstrate the effectiveness of RefineNet.RefineNet is a novel multi-path refinement network designed for high-resolution semantic segmentation. It addresses the limitation of deep CNNs, which often lose fine details during downsampling. By exploiting features at multiple levels of abstraction, RefineNet refines coarse high-level semantic features with fine-grained low-level features using long-range residual connections. This approach allows for effective end-to-end training and enables the network to capture rich background context efficiently through chained residual pooling. The method achieves state-of-the-art performance on seven public datasets, including an intersection-over-union (IoU) score of 83.4 on the challenging PASCAL VOC 2012 dataset, outperforming the best previous method, DeepLab. The paper also discusses related work, provides a detailed description of the network architecture, and presents comprehensive experimental results to demonstrate the effectiveness of RefineNet.