Knowledge Commons of Institute of Automation，CAS
| Deep Gradient Learning for Efficient Camouflaged Object Detection | |
| Ge-Peng Ji1 | |
| Source Publication | Machine Intelligence Research
 |
| ISSN | 2731-538X |
| 2023 | |
| Volume | 20Issue:1Pages:92-108 |
| Abstract | 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 (80fps) 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. |
| Keyword | Camouflaged object detection (COD) object gradient soft grouping efficient model image segmentation |
| DOI | 10.1007/s11633-022-1365-9 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50902 |
| Collection | 学术期刊_Machine Intelligence Research |
| Affiliation | 1.School of Computer Science, Wuhan University, Wuhan 430072, China 2.Computer Vision Laboratory, ETH Zürich, Zürich 8092, Switzerland |
| Recommended Citation GB/T 7714 | Ge-Peng Ji. Deep Gradient Learning for Efficient Camouflaged Object Detection[J]. Machine Intelligence Research,2023,20(1):92-108. |
| APA | Ge-Peng Ji.(2023).Deep Gradient Learning for Efficient Camouflaged Object Detection.Machine Intelligence Research,20(1),92-108. |
| MLA | Ge-Peng Ji."Deep Gradient Learning for Efficient Camouflaged Object Detection".Machine Intelligence Research 20.1(2023):92-108. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| MIR-2022-05-171.pdf（5723KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment