RefineNet is a multi-path refinement network designed for high-resolution semantic segmentation. It addresses the challenge of maintaining high-resolution details in deep neural networks by utilizing long-range residual connections to refine features across multiple levels of abstraction. The network employs residual connections with identity mappings, enabling effective end-to-end training. A key component is "chained residual pooling," which efficiently captures background context by pooling features with multiple window sizes and fusing them using learnable weights. RefineNet is tested on seven public datasets, achieving a state-of-the-art intersection-over-union (IoU) score of 83.4 on the PASCAL VOC 2012 dataset. The network's architecture allows for flexible cascading and modification, with various variants tested, including single, 2-cascaded, and 4-cascaded versions. RefineNet outperforms existing methods on multiple benchmarks, demonstrating superior performance in semantic segmentation and object parsing tasks. The model is trained using fully convolutional networks and is effective in maintaining high-resolution predictions without significant memory or computational overhead. The network's design enables efficient gradient propagation and effective feature fusion, leading to high-quality segmentation results.RefineNet is a multi-path refinement network designed for high-resolution semantic segmentation. It addresses the challenge of maintaining high-resolution details in deep neural networks by utilizing long-range residual connections to refine features across multiple levels of abstraction. The network employs residual connections with identity mappings, enabling effective end-to-end training. A key component is "chained residual pooling," which efficiently captures background context by pooling features with multiple window sizes and fusing them using learnable weights. RefineNet is tested on seven public datasets, achieving a state-of-the-art intersection-over-union (IoU) score of 83.4 on the PASCAL VOC 2012 dataset. The network's architecture allows for flexible cascading and modification, with various variants tested, including single, 2-cascaded, and 4-cascaded versions. RefineNet outperforms existing methods on multiple benchmarks, demonstrating superior performance in semantic segmentation and object parsing tasks. The model is trained using fully convolutional networks and is effective in maintaining high-resolution predictions without significant memory or computational overhead. The network's design enables efficient gradient propagation and effective feature fusion, leading to high-quality segmentation results.