姜曉燕🕺，計算機系副教授、碩導；耶拿大學（德國）計算機科學博士。研究方向⚽️🔓：計算機視覺、機器學習；研究課題：多目標檢測與跟蹤⚅、視覺SLAM、語義分割🐑、姿態估計。Applied Intelligence 期刊副主編（SCI, IF: 5.3）。曾獲德國 DAAD🧑🏻🦯➡️🐖、國家留學基金委CSC獎學金資助。
已發表論文50 余篇，包括Tran. SMC, Pattern Recognition, Trans. ITS、KBS、SPIC、EAAI、ICME🤯👨🏻🏭、ICIP等領域頂級期刊和會議；國際會議 ICPCSEE2019 的PC、國際會議CVIP 2024#️⃣、HCIVR 2024的TPC。國際會議 ICFTIC2019👨👨👧、IWITC2021、CISE2023作主旨報告；為多個頂級期刊與國際會議的評審等。作為主要參與人獲得上海市科技進步獎二等獎。申請發明專利 8 項（實審），已授權2項，授權實用新型專利 5 項。
主持/參與國家自然科學基金青年項目、重點項目👷🏽🪙、面上項目、上海市科委重點項目多項；負責和參與人工智能相關橫向項目多項🧑🏼🤝🧑🏼，應用領域廣泛，包括機器視覺、視頻監控、缺陷檢測、道路巡檢📿、智慧醫療等。現為電子與電氣工程恒达多維度人工智能科研團隊負責人。
每年招收3-5名研究生，團隊以學生發展為中心👲🏿，打牢從傳統視覺算法到深度學習及大模型相關的關鍵知識與理論♾，結合實際場景🤾🏿，培養獨立思考🏄🏿♂️，發現問題和解決問題的能力。目標為激發大家持續終身學習的內驅力♥️🈲！
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-identification 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