Summary of A Deep Semantic Segmentation Network with Semantic and Contextual Refinements, by Zhiyan Wang et al.
A Deep Semantic Segmentation Network with Semantic and Contextual Refinements
by Zhiyan Wang, Deyin Liu, Lin Yuanbo Wu, Song Wang, Xin Guo, Lin Qi
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 A novel approach to accelerate semantic segmentation in multimedia processing is proposed. The method addresses misalignment issues when restoring high-level feature maps by introducing a Semantic Refinement Module (SRM). SRM learns transformation offsets for each pixel, guided by high-resolution feature maps and neighboring offsets. This enhancement improves the semantic representation of the segmentation network, particularly at object boundaries. Additionally, a Contextual Refinement Module (CRM) captures global context information across spatial and channel dimensions. The proposed modules are validated on Cityscapes, Bdd100K, and ADE20K datasets, outperforming state-of-the-art methods. Furthermore, a lightweight segmentation network is developed, achieving an mIoU of 82.5% on the Cityscapes validation set with only 137.9 GFLOPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to analyze images and videos is being explored. This method makes it faster and more accurate by fixing a problem that happens when high-level details are restored. It does this by learning how to adjust each pixel’s position based on nearby pixels and the original, higher-resolution image. This helps the algorithm better understand what’s happening at the edges of objects. The approach also includes another module that looks at global patterns in images and videos to help with segmentation. Tests were run on three popular datasets and showed that this method performs better than others. It even works well when using fewer computer resources. |
Keywords
» Artificial intelligence » Semantic segmentation
