Summary of Convolution-based Probability Gradient Loss For Semantic Segmentation, by Guohang Shan and Shuangcheng Jia
Convolution-based Probability Gradient Loss for Semantic Segmentation
by Guohang Shan, Shuangcheng Jia
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel Convolution-based Probability Gradient (CPG) loss for semantic segmentation. It leverages convolution kernels similar to the Sobel operator to compute pixel intensity gradients, enabling the calculation of gradients for ground-truth and predicted category-wise probabilities. This approach enhances network performance by maximizing the similarity between these two probability gradients. Additionally, it extracts object boundaries based on ground-truth probability gradients and applies the CPG loss exclusively to pixels belonging to boundaries, specifically improving accuracy near object borders. The proposed CPG loss is highly convenient and effective, establishing pixel relationships through convolution and calculating errors from a distinct dimension compared to traditional pixel-wise loss functions like cross-entropy loss. This loss function is evaluated on three well-established networks (DeepLabv3-Resnet50, HRNetV2-OCR, and LRASPP_MobileNet_V3_Large) across three standard segmentation datasets (Cityscapes, COCO-Stuff, ADE20K), demonstrating consistent and significant enhancements in mean Intersection over Union. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to help machines learn about images. It uses special filters that look like the Sobel operator to understand how the pixels change as you move along the edges of objects. This helps the machine get better at finding objects in pictures. The new technique is tested on three different computer models and three large sets of images, showing big improvements. |
Keywords
» Artificial intelligence » Cross entropy » Loss function » Probability » Semantic segmentation
