Deep Gradient Learning for Efficient Camouflaged Object Detection

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 tra...

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Veröffentlicht in:International journal of automation and computing 2023-02, Vol.20 (1), p.92-108
Hauptverfasser: Ji, Ge-Peng, Fan, Deng-Ping, Chou, Yu-Cheng, Dai, Dengxin, Liniger, Alexander, Van Gool, Luc
Format: Artikel
Sprache:eng
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Zusammenfassung: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 .
ISSN:2731-538X
1476-8186
2153-182X
2731-5398
1751-8520
2153-1838
DOI:10.1007/s11633-022-1365-9