Deep Gradient Learning for Efficient Camouflaged Object Detection

• URL: http://arxiv.org/abs/2205.12853v1
• Date: Wed, 25 May 2022 15:25:18 GMT
• Title: Deep Gradient Learning for Efficient Camouflaged Object Detection
• Authors: Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger and Luc Van Gool
• Abstract summary: This paper introduces DGNet, a novel deep framework that exploits object supervision for gradient camouflaged object detection (COD) Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Results also show that the proposed DGNet performs well in polyp segmentation, defect detection, and transparent object segmentation tasks.
• Score: 152.24312279220598
• License: http://creativecommons.org/licenses/by/4.0/
• 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-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.

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