Deep Gradient Learning for Efficient Camouflaged Object Detection

Autor:	Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc Van Gool
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Camouflaged object detection (COD) Object gradient Soft grouping Efficient model Image segmentation Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition
Zdroj:	Machine Intelligence Research, 20 (1)
ISSN:	2731-538X 2731-5398
Popis:	This paper introduces deep gradient network (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. The application results also show that the proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks. The code will be made available at https://github.com/GewelsJI/DGNet. Machine Intelligence Research, 20 (1) ISSN:2731-538X ISSN:2731-5398
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb8175f9c1ea3820280d9c4850d52297 http://arxiv.org/abs/2205.12853 Zobrazit plný text záznamu Full text from SpringerLink