The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Deep Gradient Learning for Efficient Camouflaged Object Detection
Open accessAbstract
This paper introduces deep gradient network (DGNet), a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82% parameters. The application results also show that the proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks. The code will be made available at https://github.com/GewelsJI/DGNet.
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Deep Residual Learning for Image Recognition
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ImageNet classification with deep convolutional neural networks
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
PyTorch: An Imperative Style, High-Performance Deep Learning Library
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A Computational Approach to Edge Detection
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Metadata
- Authors
- Ji, G.-P., Fan, D.-P., Chou, Y.-C., Dai, D., Liniger, A., & Van Gool, L.
- Venue
- Machine Intelligence Research
- Published
- 2023-01-10
- Volume
- 20
- Issue
- 1
- Pages
- 92-108
- ISSN
- 1476-8186 (ISSN-L)
- 2731-538X
- 2731-5398
- OpenAlex
- W4315490105
- Cited by
- 284
- References
- 70
- FWCI
- 25.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ji, G.-P., Fan, D.-P., Chou, Y.-C., Dai, D., Liniger, A., & Van Gool, L. (2023). Deep Gradient Learning for Efficient Camouflaged Object Detection. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1365-9
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