Deep Gradient Learning for Efficient Camouflaged Object Detection

Abstract

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-CVPR21 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.

MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection

“…Recently, many researches put emphasis on learning from a fixed single view with either auxiliary tasks [18,32,34,58,67,15], uncertainty discovery [20,26], or vision transformers [56,38] and their proposed methods achieved significant progress. Nevertheless, due to visual insignificance of camouflaged objects and contextual insufficiency from single-view input, they are still striving to precisely recognize camouflaged objects and their performance needs to be improved.…”