Enhancing Semantic Segmentation Performance with Convolution-based Probability Gradient Loss

The paper introduces a novel Convolution-based Probability Gradient (CPG) loss function that enhances the performance of semantic segmentation networks by maximizing the similarity between the predicted and ground-truth probability gradients, particularly at object boundaries.