论文标题
深度梯度学习以进行有效伪装的对象检测
Deep Gradient Learning for Efficient Camouflaged Object Detection
论文作者
论文摘要
本文介绍了DGNET，这是一个新颖的深层框架，可利用对象梯度监督的伪装对象检测（COD）。它将任务分为两个连接的分支，即一个上下文和纹理编码器。必不可少的连接是梯度引起的过渡，代表上下文和纹理特征之间的软组。从简单但高效的框架中受益，DGNET大量优于现有的最新COD模型。值得注意的是，我们的高效版本DGNET-S实时运行（80 fps），并获得与尖端模型JCSOD-CVPR $ _ {21} $相当的结果，只有6.82％的参数。应用程序结果还表明，所提出的DGNET在息肉分割，缺陷检测和透明对象分割任务中表现良好。代码将在https://github.com/gewelsji/dgnet上提供。
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR$_{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.
