江结林，南京信息工程大学副教授，硕士生导师。2015年11月毕业于南京理工大学计算机科学与工程学院，获工学博士学位。2014年7月至2017年7月于香港理工大学做研究助理及博士后。主要研究方向为：疵点检测、图像分类、图像复原等。主持国家级、省级项目等5项，参编《计算思维导论》教材1部。Selected Publications over three years：（#co-first author，*Corresponding author）[1]S.Wei, J.Jiang*,X.Xu. Exploring high-order-aware prompt learning for zero-shot anomaly detection, AAAI, 2026.[2]S.Wei, J.Jiang*, X.Xu. UniNet: A contrastive learning-guided unified framework with feature selection for anomaly detection. In Proceedings of the IEEE/CVFconference on Computer Vision and Pattern Recognition (CVPR), pp. 9994–10003, 2025.[3]J.Jiang, X.Liu, P.Yan, S.Wei, Y.Cui*, Localize-Diffusion based dual-branch anomaly detection. Neural Networks, 107439, 2025.[4]J.Jiang, C.Ge, S.Wei, X.Liu. Dual-branch guided multi-scale half-instance normalization network for low-dose CT image denoising, Medical Physics, vol. 52, no. 10. 2025.[5]J.Jiang,S.Wei,X.Xu,Y.Cui,X.Liu.Unsupervised anomaly detection and localization based on two-hierarchy normalizing flow, IEEE Transactions on Instrumentation & Measurement, vol.73,2024.[6]J.Zhu#, P.Yan#, J.Jiang*, Y.Cui and X.Xu. Asymmetric teacher-student feature pyramid matching for industrial anomaly detection. IEEE Transactions on Instrumentation & Measurement, vol.73,2024.[7]J.Jiang et al., Masked swin transformer Unet for industrial anomaly detection, IEEE Transactions on Industrial Informatics,vol. 19, no.2, pp. 2200-2209, 2023.[8]J.Jiang, J.Guo, M.Khan, Y.Cui, W.Lin, Energy-saving service offloading for the internet of medical things using deep reinforcement learning. ACM Transactions on Sensor Networks,vol. 19, no. 23, pp. 1-20, 2023.[9]J.Jiang, H.Xu, X.Xu, Y.Cui, J.Wu, Transformer-based fused attention combined with CNNs for image classiffcation, Neural Processing Letters, vol. 55, pp. 11905-11919, 2023.[10]J.Jiang, K.Yang, X.Xu and Y.Cui,A serial attention module-based deep convolutional neural network for mixed Gaussian-impulse removal, IET Image Process, vol. 17, no. 6, pp. 1837-1851, 2023.[11]J.Jiang, X.Hong,Y.Zhao, X.Xu,Y.Cui，SDAUNet: A simple dual attention mechanism UNet for mixed noise removal, IET Image Process,vol. 17, no. 13, pp. 1837-1851, 2023.[12]J.Jiang, L.Liu, Y.Cui and Y.Zhao, A nested UNet based on multi-scale feature extraction for mixed Gaussian-impulse removal, Applied Sciences,vol. 13, no. 17, 9520, 2023.发明专利： 1. 一种基于跨模态自适应交互的少样本工业异常检测方法。专利号：ZL 2025 1 0484103.82. 一种噪声抑制模态融合对齐的工业图像异常检测方法。专利号：ZL 2025 1 0767310.4 3. 基于知识蒸馏的零样本工业异常检测方法。专利号：ZL 2025 1 0765559.1 4. 基于跨模态文本语义驱动的少样本缺陷识别方法。专利号：ZL 2025 1 1089295.9 5. 一种基于Swin Transformer结构结合SSIM和GMSD的疵点检测方法。专利号：ZL 2022 1 0535974.46. 一种异常图像样本生成方法及装置。专利号：ZL 2023 1 0621436.17. 一种多尺度互补学习的图像去噪方法及装置。专利号：ZL 2023 1 1832535.0 8. 基于距离信息驱动的复杂工业图像异常检测方法及装置。专利号：ZL 2024 1 0438275.7 9. 一种双阶段图像的异常检测与重构方法及系统。专利号：ZL 2024 1 0227319.110. 一种工业异常检测方法及系统。专利号：ZL 2024 1 0925191.6 11. 一种基于yolov5和注意力机制模型的疵点检测方法。专利号：ZL 2021 1 1316595.8 12. 一种面向文献的表格信息抽取方法。专利号：ZL 2021 1 0736883.213. 一种基于LSD线段检测的文档图像三角图检测方法。专利号：ZL 2021 1 1175439.414. 一种气象雷达杂波分类识别方法及装置。专利号：ZL 2022 1 1156539.715. 一种双分支图像异常检测方法、装置、设备及介质。专利号：ZL 2023 1 0316062.216. 一种基于改进LSD的磁极倒转图信息抽取方法。专利号：ZL 2022 1 0582218.717. 一种面向边缘分布式训练的资源动态分析与样本负载调度优化方法。专利号：ZL 2024 1 1624235.818. 一种复原式自监督疵点检测方法、装置及存储介质。专利号：ZL 2023 1 0531299.219. 一种基于自适应掩码重构的异常检测方法及装置。专利号：2023 1 1012204.2 20. 一种基于非局部注意力的深度增强图像去噪方法。专利号：2022 1 1053496.X