Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc Van Gool. Deep Gradient Learning for Efficient Camouflaged Object Detection[J]. Machine Intelligence Research, 2023, 20(1): 92-108. DOI: 10.1007/s11633-022-1365-9
Citation: Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc Van Gool. Deep Gradient Learning for Efficient Camouflaged Object Detection[J]. Machine Intelligence Research, 2023, 20(1): 92-108. DOI: 10.1007/s11633-022-1365-9

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 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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