邓晓衡

教授 博士生导师 硕士生导师

入职时间:2006-01-05

所在单位:电子信息学院

职务:院长

学历:博士研究生毕业

性别:男

联系方式:Email:dxh@csu.edu.cn

学位:博士学位

在职信息:在职

主要任职:计算机学院副院长 湖南省数据传感与交换设备工程中心 主任 IEEE RS Chapter长沙 主席CCF普适计算专委 委员 CCF长沙 执委

毕业院校:中南大学

学科:计算机科学与技术
信息与通信工程

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Zijian Liu, Xiaoheng Deng, Ping Jiang, Conghao Lv, et al. Edge Perception Camouflaged Object Detection under Frequency Domain Reconstruction[J]. IEEE Transactions on Circuits and Systems for Video Technology,,DOI: 10.1109/TCSVT.2024.3404005 , 2024

发布时间:2024-05-23

点击次数:

关键字:Camouflaged Object Detection, Salient Object Detection, Frequency Domain Reconstruction

摘要:Camouflaged object detection has been considered a challenging task due to its inherent similarity and interference from background noise. It requires accurate identification of targets that blend seamlessly with the environment at the pixel level. Although existing methods have achieved considerable success, they still face two key problems. The first one is the difficulty in removing texture noise interference and thus obtaining accurate edge and frequency domain information, leading to poor performance when dealing with complex camouflage strategies. The latter is that the fusion of multiple information obtained from auxiliary subtasks is often insufficient, leading to the introduction of new noise. In order to solve the first problem, we propose a frequency domain reconstruction module based on contrast learning, through which we can obtain high-confidence frequency domain components, thus enhancing the model’s ability to discriminate target objects. In addition, we design a frequency domain representation decoupling module for solving the second problem to align and fuse features from the RGB domain and the reconstructed frequency domain. This allows us to obtain accurate edge information while resisting noise interference. Experimental results show that our method outperforms 12 state-of-the-art methods in three benchmark camouflaged object detection datasets. In addition, our method shows excellent performance in other downstream tasks such as polyp segmentation, surface defect detection, and transparent object detection.

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发表时间:2024-05-01

上一条: Zequn Cao; Xiaoheng Deng; Sheng Yue; Ping Jiang, et al. Dependent Task Offloading in Edge Computing Using GNN and Deep Reinforcement Learning[J]. IEEE Internet of Things Journal, DOI: 10.1109/JIOT.2024.3374969

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