The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Bi-Directional Cascade Network for Perceptual Edge Detection
Open accessAbstract
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales. To extract edges at dramatically different scales, we propose a Bi-Directional Cascade Network (BDCN) structure, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all CNN outputs. Furthermore, to enrich multi-scale representations learned by BDCN, we introduce a Scale Enhancement Module (SEM) which utilizes dilated convolution to generate multi-scale features, instead of using deeper CNNs or explicitly fusing multi-scale edge maps. These new approaches encourage the learning of multi-scale representations in different layers and detect edges that are well delineated by their scales. Learning scale dedicated layers also results in compact network with a fraction of parameters. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and Multicue, and achieve ODS Fmeasure of 0.828, 1.3% higher than current state-of-the art on BSDS500.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Feature Pyramid Networks for Object Detection
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A Computational Approach to Edge Detection
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Wu, G.-K., Xu, J., Zhang, Y.-D., & Zhang, B.-P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16258-0 · Cited by 4
Metadata
- Authors
- He, J., Zhang, S., Yang, M., Shan, Y., & Huang, T.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3823-3832
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2953881420
- Cited by
- 361
- References
- 55
- FWCI
- 13.24
- Type
- conference paper
- Language
- en
- Open access
- green
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