Deep Gradient Learning for Efficient Camouflaged Object Detection
It addresses camouflaged object detection, a challenging computer vision problem, with incremental improvements in efficiency and performance for applications like medical imaging and industrial inspection.
The paper tackles camouflaged object detection by introducing DGNet, a deep framework that uses object gradient supervision to decouple context and texture features, achieving state-of-the-art performance with an efficient version running at 80 fps and comparable results using only 6.82% of parameters.
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.