  This paper introduces 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-CVPR$_{21}$ with only 6.82% parameters. Application results also show
that the proposed DGNet performs well in polyp segmentation, defect detection,
and transparent object segmentation tasks. Codes will be made available at
https://github.com/GewelsJI/DGNet.
