Summary of Sernet-former: Semantic Segmentation by Efficient Residual Network with Attention-boosting Gates and Attention-fusion Networks, By Serdar Erisen
SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks
by Serdar Erisen
First submitted to arxiv on: 28 Jan 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 This research proposes an efficient residual network architecture, Efficient-ResNet, to improve semantic segmentation in images. The proposed architecture incorporates attention-boosting gates and modules to fuse global and local context information. Additionally, the decoder network features attention-fusion networks inspired by the AbM design. This approach is tested on challenging datasets such as CamVid and Cityscapes, achieving state-of-the-art results with a mean IoU of 84.62% on CamVid and 87.35% on Cityscapes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand images by improving how they do semantic segmentation. Semantic segmentation is like coloring in the right parts of an image based on what’s there. The researchers came up with a new way to make it faster and more accurate using something called Efficient-ResNet. They tested their idea on two big datasets and got really good results! |
