The paper introduces a novel approach called CRF-RNN, which combines Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) to address pixel-level labeling tasks such as semantic segmentation. The key contribution is reformulating mean-field approximate inference for dense CRFs with Gaussian pairwise potentials as a Recurrent Neural Network (RNN). This allows the CRF-RNN to be integrated into a CNN, forming a deep network that can be trained end-to-end using back-propagation. The CRF-RNN refines coarse outputs from the CNN and passes error differentials back to the CNN during training, enhancing the overall performance. The proposed method achieves state-of-the-art results on the Pascal VOC 2012 segmentation benchmark, demonstrating the effectiveness of combining CNNs and CRFs in a unified framework.The paper introduces a novel approach called CRF-RNN, which combines Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) to address pixel-level labeling tasks such as semantic segmentation. The key contribution is reformulating mean-field approximate inference for dense CRFs with Gaussian pairwise potentials as a Recurrent Neural Network (RNN). This allows the CRF-RNN to be integrated into a CNN, forming a deep network that can be trained end-to-end using back-propagation. The CRF-RNN refines coarse outputs from the CNN and passes error differentials back to the CNN during training, enhancing the overall performance. The proposed method achieves state-of-the-art results on the Pascal VOC 2012 segmentation benchmark, demonstrating the effectiveness of combining CNNs and CRFs in a unified framework.