The paper introduces Bayesian SegNet, a deep learning framework for probabilistic pixel-wise semantic segmentation. The authors highlight the importance of modeling uncertainty in semantic segmentation for decision-making and scene understanding. Bayesian SegNet uses Monte Carlo sampling with dropout at test time to approximate the posterior distribution of pixel class labels, providing a measure of model uncertainty. This approach improves segmentation performance by 2-3% across various state-of-the-art architectures such as SegNet, FCN, and Dilation Network, particularly on smaller datasets. The paper demonstrates the effectiveness of Bayesian SegNet on benchmark datasets like CamVid and SUN RGB-D, showing higher model uncertainty at object boundaries and with visually ambiguous objects. The method is also shown to be applicable to other deep learning architectures, achieving significant improvements in segmentation accuracy. The authors conclude that Bayesian SegNet provides a reliable measure of model uncertainty and outperforms both shallow and deep architectures in semantic segmentation tasks.The paper introduces Bayesian SegNet, a deep learning framework for probabilistic pixel-wise semantic segmentation. The authors highlight the importance of modeling uncertainty in semantic segmentation for decision-making and scene understanding. Bayesian SegNet uses Monte Carlo sampling with dropout at test time to approximate the posterior distribution of pixel class labels, providing a measure of model uncertainty. This approach improves segmentation performance by 2-3% across various state-of-the-art architectures such as SegNet, FCN, and Dilation Network, particularly on smaller datasets. The paper demonstrates the effectiveness of Bayesian SegNet on benchmark datasets like CamVid and SUN RGB-D, showing higher model uncertainty at object boundaries and with visually ambiguous objects. The method is also shown to be applicable to other deep learning architectures, achieving significant improvements in segmentation accuracy. The authors conclude that Bayesian SegNet provides a reliable measure of model uncertainty and outperforms both shallow and deep architectures in semantic segmentation tasks.