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: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:信息与通信工程
计算机科学与技术

曾获荣誉:

2024-12-31  当选:  湖南省“芙蓉学者”特聘教授

2020-12-31  当选:  中南大学励志教师奖励

2010-12-31  当选:  湖南省青年骨干教师

2008-12-31  当选:  2008年获西南铝业优秀教师奖

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Ping Jiang, Xiaoheng Deng, Shaohua Wan, Huamei Qi, Shichao Zhang, "Confidence-Enhanced Mutual Knowledge for Uncertain Segmentation," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2023.3309600. IEEE Transactions on Intelligent Transportation Systems. (中科院 1区, 2023)

发布时间:2024-03-13

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摘要:Abstract— It is inevitable to recognize objects in adverse weather conditions where the uncertainty of contour areas is increased. Although some multi-task learning frameworks have gained from the directional supervision between boundary detection and semantic segmentation, the interaction between those two tasks is poorly investigated. Moreover, the performance of the contour detection is expected to degrade under foggy scenarios, because the auxiliary task also has no benefits from the main task. To address the potential risk in intelligent transportation systems, this paper proposes a mutual learning framework, named CE-MGN (Confidence-Enhanced Mutual Graph Network), to propagate confidence through continuous interaction between different tasks rather than only focusing on the accuracy of the main task. The CE-MGN perform an end-to-end training paradigm and jointly learns two tasks, contour detection and semantic segmentation, through pairwise confidence-enhancement mechanism. Moreover, the task interaction is converted into graph space to further relieve the information loss during the feature aggregation in Euclidean space. Such a framework is capable to improve the robustness of respective tasks because of the encouragement from its peer task. Extensive experiments show that our CE-MGN achieved mean IoU scores of 79.35% and 79.03% on CityScapes and Foggy CityScapes datasets, respectively. Besides, our models have a stable performance on different weather severity, where the performance fluctuation is less than 1%.

备注:http://faculty.csu.edu.cn/dengxiaoheng/zh_CN/lwcg/10445/content/49305.htm

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附件:

  • 4-Confidence-Enhanced_Mutual_Knowledge_for_Uncertain_Segmentation.pdf

  • 上一条: Yunlong Zhao, Xiaoheng Deng, Xinjun Pei, Xuechen Chen, Deng Li, "Parallel Gradient Blend for Class Incremental Learning," in IEEE International Conference on Image Processing (ICIP), doi: 10.1109/ICIP49359.2023.10222727. IEEE International Conference on Image Processing (ICIP). (CCF-B会议, 2023)

    下一条: Weishang Wu, Xiaoheng Deng, Ping Jiang, Shaohua Wan, Yuanxiong Guo, "CrossFuser: Multi-Modal Feature Fusion for End-to-End Autonomous Driving Under Unseen Weather Conditions," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2023.3307589. IEEE Transactions on Intelligent Transportation Systems. (中科院 1区, 2023)