Summary of A Feature Refinement Module For Light-weight Semantic Segmentation Network, by Zhiyan Wang et al.
A feature refinement module for light-weight semantic segmentation network
by Zhiyan Wang, Xin Guo, Song Wang, Peixiao Zheng, 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)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel semantic segmentation method proposed in this paper aims to improve the representation ability of light-weight networks while maintaining high accuracy and low computational complexity. A feature refinement module (FRM) is introduced, which extracts semantics from multi-stage feature maps generated by the backbone network and captures non-local contextual information using a transformer block. Experimental results on Cityscapes and Bdd100K datasets demonstrate that this method achieves a promising trade-off between accuracy and computational cost, with 80.4% mIoU achieved on the Cityscapes test set at only 214.82 GFLOPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to improve semantic segmentation in images. It creates a special module that helps light-weight networks understand what’s happening in an image better. This is important because we want computers to be able to quickly and accurately identify objects in pictures. The researchers tested their method on two big datasets, Cityscapes and Bdd100K, and showed that it works well. |
Keywords
» Artificial intelligence » Semantic segmentation » Semantics » Transformer
