Convolution-based Probability Gradient Loss for Semantic Segmentation
Guohang Shan Shuangcheng Jia
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.