In this paper, we introduce a novel Convolution-based Probability Gradient
(CPG) loss for semantic segmentation. It employs convolution kernels similar to
the Sobel operator, capable of computing the gradient of pixel intensity in an
image. This enables the computation of gradients for both ground-truth and
predicted category-wise probabilities. It enhances network performance by
maximizing the similarity between these two probability gradients. Moreover, to
specifically enhance accuracy near the object's boundary, we extract the object
boundary based on the ground-truth probability gradient and exclusively apply
the CPG loss to pixels belonging to boundaries. CPG loss proves to be highly
convenient and effective. It establishes pixel relationships through
convolution, calculating errors from a distinct dimension compared to
pixel-wise loss functions such as cross-entropy loss. We conduct qualitative
and quantitative analyses to evaluate the impact of the CPG loss on three
well-established networks (DeepLabv3-Resnet50, HRNetV2-OCR, and
LRASPP_MobileNet_V3_Large) across three standard segmentation datasets
(Cityscapes, COCO-Stuff, ADE20K). Our extensive experimental results
consistently and significantly demonstrate that the CPG loss enhances the mean
Intersection over Union.

本文介绍了一种新颖的基于卷积的概率梯度（CPG）损失方法，它使用类似 Sobel 算子的卷积核计算图像中像素强度的梯度，从而能够计算真值和预测的类别概率之间的梯度。这种方法通过最大化这两个概率梯度之间的相似性来提高网络性能。此外，为了专门增强物体边界附近的准确性，我们根据真值概率梯度提取物体边界，并仅将 CPG 损失应用于属于边界的像素。CPG 损失被证明非常方便和有效，通过卷积建立像素间的关系，并且与像素级损失函数（例如交叉熵损失）相比，从独特的维度计算错误。我们对三个常用的分割数据集（Cityscapes，COCO-Stuff，ADE20K）中三个已建立网络（DeepLabv3-Resnet50，HRNetV2-OCR，LRASPP_MobileNet_V3_Large）的 CPG 损失的影响进行了定性和定量分析，广泛的实验结果一致且明显地表明 CPG 损失提高了平均交并比。