Summary of Cycle Pixel Difference Network For Crisp Edge Detection, by Changsong Liu et al.
Cycle Pixel Difference Network for Crisp Edge Detection
by Changsong Liu, Wei Zhang, Yanyan Liu, Mingyang Li, Wenlin Li, Yimeng Fan, Xiangnan Bai, Liang Zhang
First submitted to arxiv on: 6 Sep 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 proposed CPD-Net model addresses two significant issues in deep learning-based edge detection: reliance on large-scale pre-trained weights and generation of thick edges. The model uses a U-shape encoder-decoder architecture, incorporating a novel cycle pixel difference convolution (CPDC) to eliminate the need for pre-trained weights. Additionally, a multi-scale information enhancement module (MSEM) and dual residual connection-based (DRC) decoder enhance edge location ability and generate crisp contour maps. Experimental results on four standard benchmarks demonstrate competitive performance, including ODS scores of 0.813 on BSDS500 and 0.898 on BIPED. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect edges in images is being developed using a special kind of artificial intelligence called deep learning. This method tries to solve two problems: it doesn’t need to rely on other, bigger models to work, and it produces clear, thin edges instead of thick ones. The team behind this project created a model that uses information from the edge itself to improve its performance, which leads to better results. They tested their model on several datasets and got good scores, showing that it can be used for real-world applications. |
Keywords
» Artificial intelligence » Decoder » Deep learning » Encoder decoder
