Deep Gradient Learning for Efficient Camouflaged Object Detection

Ji, Ge-Peng; Fan, Deng-Ping; Chou, Yu-Cheng; Dai, Dengxin; Liniger, Alexander; Van Gool, Luc

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Deep Gradient Learning for Efficient Camouflaged Object Detection

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Dai, Dengxin
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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arXiv:2205.12853.pdf
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Citation

Ji, G.-P., Fan, D.-P., Chou, Y.-C., Dai, D., Liniger, A., & Van Gool, L. (2022). Deep Gradient Learning for Efficient Camouflaged Object Detection. Retrieved from https://arxiv.org/pdf/2205.12853.pdf.

Cite as: https://hdl.handle.net/21.11116/0000-000C-1B97-C

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.