亚博取款高效快速

亚博取款高效快速:姜晓燕

发布者:赵葳浏览次数:2565


姜晓燕,计算机系副教授、硕导;耶拿大学(德国)计算机科学博士。研究方向:计算机视觉、机器学习;研究课题:多目标检测与跟踪、视觉SLAM、语义分割、姿态估计。Applied Intelligence 期刊副主编(SCI, IF: 5.3)。曾获德国 DAAD、国家留学基金委CSC奖学金资助。

已发表论文50 余篇,包括Tran. SMC, Pattern Recognition, Trans. ITS、KBS、SPIC、EAAI、ICME、ICIP等领域顶级期刊和会议;国际会议 ICPCSEE2019 的PC、国际会议CVIP 2024、HCIVR 2024的TPC。国际会议 ICFTIC2019、IWITC2021、CISE2023作主旨报告;为多个顶级期刊与国际会议的评审等。作为主要参与人获得上海市科技进步奖二等奖。申请发明专利 8 项(实审),已授权2项,授权实用新型专利 5 项。

主持/参与国家自然科学基金青年项目、重点项目、面上项目、上海市科委重点项目多项;负责和参与人工智能相关横向项目多项,应用领域广泛,包括机器视觉、视频监控、缺陷检测、道路巡检、智慧医疗等。现为电子与电气工程亚博取款高效快速多维度人工智能科研团队负责人。

每年招收3-5名研究生,团队以学生发展为中心,打牢从传统视觉算法到深度学习及大模型相关的关键知识与理论,结合实际场景,培养独立思考,发现问题和解决问题的能力。目标为激发大家持续终身学习的内驱力!


主要成果:

[J1] TV-Net: A Structure-level Feature Fusion Network based on Tensor Voting for Road Crack Segmentation. W. Zheng, X. Jiang*#, Z. Fang, and Y. Gao. IEEE Transactions on Intelligent Transportation Systems (TITS), Impact Factor: 8.5, pp.:1-12,2024

[J2] A Multi-Scale Coarse-to-Fine Human Pose Estimation Network with Hard Keypoint Mining. X. Jiang, H. Tao, J. Hwang, and Z. Fang. IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC), Impact Factor: 8.7. pp.: 1730-1741, 2023.3

[J3] An Automatic Prompt Generation for Specific Classes based on Visual Language Pre-training Models. B. Han, X. Jiang*, Z. Fang, H. Fujita, and Y. Gao. Pattern Recognition. pp.: 1-11, IF: 8, 2023

[J4] A Sparse Graph Wavelet Convolution Neural Network for Video-based Person Re-identification. Y. Yao, X. Jiang*, H. Fujita, and Z. Fang. Pattern Recognition, Impact factor: 8, Vol: 129, pp.: 1-12, 2022

[J5] Effective Person Re-identi?cation by Self-Attention Model Guided Feature Learning. Y. Li, X. Jiang#*, and J. Hwang. Knowledge-Based Systems, Impact factor: 8.8, Vol: 187, 2020

[J6] Multi-Marker Tracking for Large-scale X-ray Stereo Video Data. X. Jiang, M. Simon, Y. Yang, and J. Denzler. Signal Processing: Image Communication. 59(2017): 140-149, 2017

[J7] Photometric Transfer for Direct Visual Odometry. K. Zhu, X. Jiang*, Z. Fang, Y. Gao, H. Fujita, and J. Hwang. Knowledge-Based Systems. IF: 8.8, Vol: 213, 2021

[C1] LiDUT-Depth: A Lightweight Self-supervised Depth Estimation Model featuring Dynamic. Upsampling and Triplet Loss Optimization. Hao Jiang, Xuan Shao, Zhijun Fang, and Xiaoyan Jiang. ICPR2024

[C2] Depth Estimation of Multi-modal Scene based on Multi-scale Modulation. A. Wang, Z. Fang, X. Jiang, Y. Gao, C. Shao, G. Cao, and S. Ma. IEEE International Conference on Image Processing (ICIP), London, UK, 2023.

[C3] Unsupervised learning of depth and ego-motion with spatial-temporal geometric constraints. A. Wang, Y. Gao, Z. Fang*, X. Jiang*, S. Wang, S. Ma, and J. Hwang. International Conference on Multimedia and Expo (ICME), Shanghai China. 2019



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