江俊君

基本信息 Publication 新建主栏目 Biography 名称 Dr. Jiang is currently a full Professor in the School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. Before that he was a Project Researcher with the National Institute of Informatics (NII), Tokyo, Japan. He obtained his Ph.D. from School of Computer, Wuhan University, China, in 2014. His current interests mainly focus on computer vision, image processing and hyperspectral image analysis, etc. He has authored and co-authored more than 100 scientific articles. Dr. Jiang is the Area Editor of Information Fusion, the Associate Editor of Remote Sensing and the Early Career Advisory Board (ECAB) member of IEEE/CAA Journal of Automatica Sinica (JAS). Dr. Jiang received the Wu Wenjun Artificial Intelligence Excellent Youth Award 2020, China Computer Federation (CCF) Outstanding Doctoral Dissertation Award 2016 and ACM Wuhan Doctoral Dissertation Award 2015. Dr. Jiang was the recipient of the Finalist of the World's FIRST 10K Best Paper Award at ICME 2017, the Best Student Paper Runner-up Award at MMM 2015, and the Best Paper Award at IFTC 2018. Dr. Jiang has been identified in the 2021/2022 Highly Cited Researcher lists from Clarivate, and has been also ranked as the World's Top 2% most-cited scientists by Stanford University. 江俊君，哈尔滨工业大学计算学部长聘教授、博士生导师、智能接口与人机交互研究中心副主任，入选国家级青年人才计划，哈尔滨工业大学“青年科学家工作室”学术带头人。2014年12月于武汉大学计算机学院获得博士学位，2016年至2018年在日本国立情報学研究所担任特任研究员。研究方向主要包括图像处理、计算机视觉、深度学习（研究侧重高光谱图像处理与分析、三维场景理解与重建、多源信息感知与融合等）。相关研究成果发表IEEE Transactions期刊论文和CCF A类会议论文百余篇，发表论文被谷歌学术引用14000余次，H因子为58，连续三年入选全球高被引科学家和中国高被引学者。出版人脸超分辨率和高光谱图象处理专著各1部，获得国家发明专利授权21项。担任Information Fusion、The Visual Computer、Remote Sensing等国际期刊编委和自动化学报 (英文版)青年编委。曾获吴文俊人工智能优秀青年奖、中国计算机学会优秀博士论文奖、ACM-武汉优秀博士论文奖、湖北省优秀博士论文奖、ICME 2017最佳论文提名、MMM 2015最佳学生论文奖亚军、IFTC 2018 最佳论文奖，2016年获湖北省科技进步一等奖。在国际会议挑战赛中获得八次冠亚军。主持国家重点研发计划课题和国家自然科学基金联合重点/面上/青年项目。 欢迎报考2024年秋季入学的博士研究生、硕士研究生。研究方向包括图像处理、计算机视觉和深度学习等。 News!!! 名称 l [Call for Papers] Submission Deadline: Mar. 29, 2024. Remote Sensing--Special Issue "Deep Learning for the Analysis of Multi-/Hyperspectral Images II". l 2024.02, Two papers were accepted to CVPR 2024. Congratulate to Yuchen and Jia Han. l 2024.01, One paper was accepted to IEEE TNNLS. Congratulate to Pengwei. l 2023.12, Three papers were accepted to AAAI 2024. Congratulate to Gang Wu, Chenyang, and Kui Jiang. l 2023.11, One paper was accepted to Information Fusion. Congratulate to Qing Ma. l 2023.06, One paper was accepted to IEEE TNNLS. Congratulate to Chenyang. l 2023.03, One paper was accepted to CVPR 2023. Congratulate to Chenyang. Newly Accepted and Preprint Papers 名称 [1] Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu, “Fully 1 × 1 Convolutional Network for Lightweight Image Super-Resolution,” Machine Intelligence Research, 2024. [2] Y. Pan, J. Jiang*, K. Jiang, Z. Wu, K. Yu, and X. Liu, “OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition,” CVPR 2024. [3] Jiahan Li, Jiuyang Dong, ShenjinHuang, Xi Li, Junjun Jiang, Xiaopeng Fan, Yongbing Zhang, “Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly-supervised Learning,” CVPR 2024. [4] P. Liang, J. Jiang*, X. Liu, and J. Ma, “Image Deblurring by Exploring In-depth Properties of Transformer,” IEEE TNNLS, 2024. (code) [5] G. Wu, J. Jiang*, K. Jiang, X. Liu, “Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration,” AAAI 2024. (code) [6] C. Wang, J. Jiang*, K. Jiang, X. Liu, “Low-Light Face Super-Resolution via Illumination, Structure, and Texture Associated Representation,” AAAI 2024. [7] K. Jiang, J. Jiang*, X. Liu, X. Xu, and X. Ma “FMRNet: Image Deraining via Frequency Mutual Revision,” AAAI 2024. [8] Q. Ma, J. Jiang*, X. Liu, and J. Ma, “Reciprocal transformer for hyperspectral and multispectral image fusion,” vol. 100, Information Fusion, 2023. (code) [9] G. Wu, J. Jiang*, X. Liu, and J. Ma, “A Practical Contrastive Learning Framework for Single Image Super-Resolution,” IEEE Transactions on Neural Networks and Learning Systems, 2023. (code) [10] C. Wang, J. Jiang*, Z. Zhong, and X. Liu, “Spatial-Frequency Mutual Learning for Face Super-Resolution,” CVPR 2023. (code) [11] X. Hu, J. Jiang*, X. Liu, J. Ma, “ZMFF: Zero-Shot Multi-Focus Image Fusion,” Information Fusion. (Code) Professional Services 名称 Area Editor: Information Fusion Associate Editor/Editorial Board Member: Remote Sensing, The Visual Computer Early Career Advisory Board: IEEE/CAA Journal of Automatica Sinica AC: ICME 2021/2022 TPC: ACM MM 2018/2019/2020, IJCAI 2018/2021/2022, SDM 2018/2019, ICME 2019/2020, ACM MM Asia 2019, AAAI 2020/2021/2022 IEEE/CCF Senior Member Awards 名称 2023 CVPR PBVS红外图像超分辨率挑战赛冠军 2022 ACMMM 对话人像视频生成挑战赛Listening Head Generation 赛道第二名 2022 Mobile AI & AIM 2022深度估计挑战赛亚军 2022 CVPR 2022 NTIRE 图像补全挑战赛亚军 2021 ACM ICMR 2021真实场景深度图像超分辨率挑战赛冠军 2020 吴文俊人工智能优秀青年奖 2019 ICCV 2019 AIM国际视频超分辨率挑战赛亚军 2018 数字电视与无线多媒体通信国际论坛（IFTC）最佳论文奖 2017 入选哈尔滨工业大学青年拔尖人才（教授）选聘计划 2017 国际多媒体与博览会议（ICME）最佳论文奖提名奖 2016 中国计算机学会优秀博士学位论文奖 2016 湖北省优秀博士学位论文奖 2015 ACM-武汉优秀博士学位论文奖 2015 国际多媒体建模会议（MMM）最佳学生论文亚军奖 Papers&Codes 名称 Full paper list: Google Scholar n Image Super-Resolution [1] P. Liang, J. Jiang*, X. Liu, and J. Ma, “Image Deblurring by Exploring In-depth Properties of Transformer,” IEEE TNNLS, 2024. (code) [2] J. Jiang*, C. Wang, X. Liu, and J. Ma, “Deep Learning-based Face Super-resolution: A Survey,” ACM Computing Surveys, vol. 55, no. 1, pp. 1-36, 2023. [3] C. Wang, J. Jiang*, Z. Zhong, and X. Liu, “Spatial-Frequency Mutual Learning for Face Super-Resolution,” CVPR 2023. (Code) [4] G. Wu, J. Jiang*, K. Jiang, X. Liu, “Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration,” AAAI 2024. (code) [5] C. Wang, J. Jiang*, K. Jiang, X. Liu, “Low-Light Face Super-Resolution via Illumination, Structure, and Texture Associated Representation,” AAAI 2024. [6] C. Wang, J. Jiang, Z. Zhong, D. Zhai, X. Liu, “Super-Resolving Face Image by Facial Parsing Information,” accepted by IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 5, no. 4, pp. 435-448, 2023. [7] C. Wang, J. Jiang*, Z. Zhong, and X. Liu, “Propagating Facial Prior Knowledge for Multi-Task Learning in Face Super-Resolution,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 11, pp. 7317 – 7331, 2023. (code) [8] Z. Zhong, X. Liu, J. Jiang, D. Zhao, X. Ji, “Guided Depth Map Super-resolution: A Survey,” vo. 55, no. 14, ACM Computing Surveys, 2023. [9] Z. Zhong, X. Liu, J. Jiang, D. Zhao, X. Ji, “Deep Attentional Guided Image Filtering,” IEEE Transactions on Neural Networks and Learning Systems. [10] G. Wu, J. Jiang*, X. Liu, and J. Ma, “A Practical Contrastive Learning Framework for Single Image Super-Resolution,” IEEE Transactions on Neural Networks and Learning Systems, 2023. (code) [11] M. Hu, K. Jiang, L. Liao, J. Xiao, J. Jiang, Z. Wang, “Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning,” CVPR, 2022. [12] Z. Zhong, X. Liu, J. Jiang, D. Zhao, Z. Chen, X. Ji, “High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion,” IEEE Transactions on Image Processing, vol. 31, pp. 648-663, 2022. [13] P. Yi, Z. Wang, K. Jiang, J. Jiang, T. Lu, X. Tian, and J. Ma, “Omniscient Video Super-Resolution,” ICCV 2021. [14] T. Lu, Y. Wang, Y. Zhang, Y. Wang, L. Wei, Z. Wang, and J. Jiang, “Face Hallucination via Split-Attention in Split-Attention Network,” in ACM MM 2021. [15] P. Yi, Z. Wang, K. Jiang, J. Jiang, T. Lu, and J. Ma, “A Progressive Fusion Generative Adversarial Network for Realistic and Consistent Video Super-Resolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 2264 – 2280, 2022. (code) [16] K. Jiang, Z. Wang, P. Yi, J. Jiang, T. Lu, and Z. Xiong, “Dual-path Deep Fusion Network for Face Image Hallucination,” IEEE Transactions on Neural Networks and Learning Systems, vo. 33, no. 1, pp. 378-391, 2022. [17] J. Jiang, Y. Yu, S. Tang, J. Ma, A. Aizawa, and K. Aizawa, “Context-Patch based Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning,” IEEE Transactions on Cybernetics, vol. 50, no. 1, pp. 324-337, 2020. (code) [18] J. Jiang, Y. Yu, Z. Wang, S. Tang, J. Ma, “Ensemble Super-Resolution with A Reference Dataset,” IEEE Transactions on Cybernetics, vol. 50, no. 11, pp. 4694 – 4708, 2020. (code) [19] J. Ma, X. Wang, and J. Jiang*, “Image Superresolution via Dense Discriminative Network,” IEEE Transactions on Industrial Electronics, vol. 67, no. 7, pp. 5687-5695, 2020. (code) [20] L. Chen, J. Pan, J. Jiang, J. Zhang, and Y. Wu, “Robust Face Super-Resolution via Position Relation Model Based on Global Face Context,” IEEE Transactions on Image Processing, vol. 29, pp. 9002-9016, 2020. [21] K. Jiang, Z. Wang, P. Yi, G. Wang, K. Gu and J. Jiang, “ATMFN: Adaptive-threshold-based Multi-model Fusion Network for Compressed Face Hallucination,” IEEE Transactions on Multimedia, vol. 22, no. 10, pp. 2734-2747, 2020. (code) [22] J. Jiang, Y. Yu, Z. Wang, X. Liu, and J. Ma, “Graph-Regularized Locality-Constrained Joint Dictionary and Residual Learning for Face Sketch Synthesis,” IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 628-641, 2019. (Source) [23] Z. Wang, P. Yi, K. Jiang, J. Jiang, Z. Han, T. Lu, and J. Ma, “Multi-Memory Convolutional Neural Network for Video Super-Resolution,” IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2530-2544, 2019. (code) [24] K. Jiang, Z. Wang, P. Yi, G. Wang, T. Lu, and J. Jiang, “Edge-Enhanced GAN for Remote Sensing Image Superresolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 8, pp. 5799-5812, 2019. (code) [25] P. Yi, Z. Wang, K. Jiang, J. Jiang, andJ. Ma, “Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations,” in ICCV, Oct. 2019 (Oral, Acceptance rate: 4.3%). [26] J. Jiang, Y. Yu, J. Hu, S. Tang, and J. Ma, “Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination,” in IJCAI, pp. 771-778, 2018. (code) [27] J. Jiang, J. Ma, C. Chen, X. Jiang, and Z. Wang, “Noise Robust Face Image Super-resolution through Smooth Sparse Representation,” IEEE Transactions on Cybernetics, vol. 47, no. 11, pp. 3991-4002, 2017. (code) [28] J. Jiang, C. Chen, J. Ma, Z. Wang, Z. Wang, and R. Hu, “SRLSP: A Face Image Super-Resolution Algorithm Using Smooth Regression with Local Structure Prior,” IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 27-40, 2017. (code) [29] J. Jiang, X. Ma, C. Chen, L. Tao, Z. Wang, and J. Ma, “Single Image Super-Resolution via Locally Regularized Anchored Neighborhood Regression and Nonlocal Means,” IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 15-26, 2017. (code) [30] J. Jiang, Y. Yu, S. Tang, J. Ma, G. Qi, and A. Aizawa, “Context-Patch based Face Hallucination via Thresholding Locality-Constrained Representation and Reproducing Learning,” in International Conference on Multimedia and Expo (ICME 2017), Hong Kong, China, Jul 2017. (Finalist of the World's FIRST 10K Best Paper Award) (code) [31] J. Jiang, R. Hu, Z. Wang, Z. Han, and J. Ma, “Facial Image Hallucination through Coupled-Layer Neighbor Embedding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 9, pp. 1674-1684, 2016. (code) [32] J. Jiang, R. Hu, Z. Han, L. Chen, and J. Chen, “Coupled Discriminant Multi-Manifold Analysis (CDMMA) with Application to Low-Resolution Face Recognition,” in Proceedings of the 21th International Conference on Multimedia Modelling (MMM 2015), Sydney, Australia, pp. 37-48, Jan 2015. (Best Student Paper Runner-up Award) (code) [33] J. Jiang, R. Hu, Z. Wang, and Z. Han, “Face Super-Resolution via Multilayer Locality-constrained Iterative Neighbor Embedding and Intermediate Dictionaries Learning,” IEEE Transactions on Image Processing, vol.23, no.10, pp. 4220-4231, 2014. (code) [34] J. Jiang, R. Hu, Z. Wang, and Z. Han, “Noise Robust Face Hallucination via Locality-constrained Representation,” IEEE Transactions on Multimedia, vol. 16, no. 5, pp. 1268-1281, 2014. (code) [35] Z. Wang, R. Hu, S. Wang, and J. Jiang, “Face Hallucination via Weighted Adaptive Sparse Regularization,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 5, pp. 802-813, May 2014. [36] Z. Han#, J. Jiang#, R. Hu, T. Lu, and K. Huang, “Face Image Super-Resolution via Nearest Feature Line,” in ACM MM, Nara, Japan, pp. 769-772, Nov 2012. (# indicates equal contribution). (code) n Image Fusion [37] X. Hu, J. Jiang*. X. Liu .J. Ma, “ZMFF: Zero-Shot Multi-Focus Image Fusion,” Information Fusion. (Code) [38] P. Liang. J. Jiang*, X. Liu .J. Ma, “Fusion from Decomposition: A Self-Supervised Decomposition Approach for lmage Fusion,” ECCV 2022. [39] P. Liang, J. Jiang*, X. Liu, and J. Ma, “BaMBNet: A Blur-aware Multi-branch Network for Dual-Pixel Defocus Deblurring,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 5, pp. 878–892, 2022. [40] H. Zhang, H. Xu, X. Tian, J. Jiang, and J. Ma*. “Image fusion meets deep learning: A survey and perspective,” Information Fusion, vol. 76, pp. 323-336, Dec. 2021. [41] J. Ma, Z. Le, X. Tian, and J. Jiang*, “SMFuse: Multi-focus Image Fusion via Self-supervised Mask-optimization,” IEEE Transactions on Computational Imaging, vol. 7, pp. 309-320, 2021. (code) [42] H. Xu, J. Ma, Z. Shao, H. Zhang, J. Jiang, and X. Guo, “SDPNet: A Deep Network for Pan-sharpening with Enhanced Information Representation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 4120-4134, 2021. (code). [43] J. Ma, P. Liang, W. Yu, C. Chen, X. Guo, J. Wu, and J. Jiang*, “Infrared and visible image fusion via detail preserving adversarial learning,” Information Fusion, vol. 54, pp. 85--98, 2020. (code) [44] H. Xu, J. Ma, J. Jiang, X. Guo, and H. Ling, “U2Fusion: A Unified Unsupervised Image Fusion Network,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 1, pp. 502 – 518, 2022. [45] J. Ma, H. Xu, J. Jiang, X. Mei, and X. Zhang, “DDcGAN: A Dual-discriminator Conditional Generative Adversarial Network for Multi-resolution Image Fusion,” IEEE Transactions on Image Processing, vol. 29, pp. 4980-4995, 2020. (code) [46] H. Xu, J. Ma, Z. Le, J. Jiang, and X. Guo, “FusionDN: A Unified Densely Connected Network for Image Fusion", in AAAI, pp. 12484-12491, 2020 (code). [47] H. Xu, P. Liang, W. Yu, J. Jiang, and J. Ma, “Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators,” in IJCAI, pp. 3954-3960, Aug. 2019 (code). [48] J. Ma, W. Yu, P. Liang, C. Li, and J. Jiang*, “FusionGAN: A generative adversarial network for infrared and visible image fusion,” Information Fusion, vol. 48, pp. 11-26, 2019. (code) n Hyperspectral Image Processing and Analysis [49] Q. Ma, J. Jiang*, X. Liu, and J. Ma, “Reciprocal transformer for hyperspectral and multispectral image fusion,” vol. 100, Information Fusion, 2023. (code) [50] Q. Ma, J. Jiang*, X. Liu, and J. Ma, “Learning A 3D-CNN and Transformer Prior for hyperspectral Image Super-Resolution,” vol. 100, Information Fusion, 2023. (code) [51] C. Ma, J. Jiang*, H. Li*, W. Cui, and G. Li, “Progressive Token Reduction and Compensation for Hyperspectral Image Representation,” IEEE Transactions on Geoscience and Remote Sensing, 61, pp. 5502814, 2023. [52] Q. Ma, J. Jiang*, X. Liu, and J. Ma, “Multi-task Interaction learning for Spatiospectral Image Super-Resolution,” IEEE Transactions on Image Processing, vol. 31, pp. 2950-2961, 2022. [53] Q. Ma, J. Jiang*, X. Liu, and J. Ma, “Deep Unfolding Network for Spatiospectral Image Super-Resolution,” IEEE Transactions on Computational Imaging, vol. 8, pp. 28-40, 2022. (code) [54] J. Jiang, J. Ma, and X. Liu, “Multilayer Spectral-Spatial Graphs for Label Noisy Robust Hyperspectral Image Classification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 839 - 852, 2022. (code) [55] X. Wang, Y. Cheng, X. Mei, J. Jiang, and J. Ma, “Group Shuffle and Spectral-Spatial Fusion for Hyperspectral Image Super-Resolution,” IEEE Transactions on Computational Imaging, 8, pp. 1223-1236, Dec. 2022. [56] X. Wang, Q. Hu, J. Jiang, and J. Ma, “A Group-based Embedding Learning and Integration Network for Hyperspectral Image Super-resolution,” IEEE Transactions on Geoscience and Remote Sensing, 60, pp. 5541416, Nov. 2022. [57] X. Wang, J. Ma, and J. Jiang, “Recurrent Feedback Embedding Network for Hyperspectral Image Super-resolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2021. [58] X. Zhang, X. Jiang, J. Jiang, Y. Zhang, X. Liu, and Z. Cai, “Spectral-Spatial and Superpixelwise PCA for Unsupervised Feature Extraction of Hyperspectral Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2021. [59] J. Jiang, H. Sun, X. Liu, and J. Ma, “Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery,” IEEE Transactions on Computational Imaging, vol. 6, pp. 1082-1096, 2020. (paper: arXiv, IEEE Xplore; code) [60] Q. Leng, H. Yang, J. Jiang*, and Q. Tian, “Adaptive Multiscale Segmentations for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 8, pp. 5847-5860, 2020. [61] J. Jiang, J. Ma, Z. Wang, C. Chen, and X. Liu, “Hyperspectral Image Classification in the Presence of Noisy Labels,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 2, pp. 851-865, 2019. (code) [62] J. Jiang, J. Ma, C. Chen, Z. Wang, Z. Cai, and L. Wang, “SuperPCA: A Superpixelwise Principal Component Analysis Approach for Unsupervised Feature Extraction of Hyperspectral Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4581-4593, 2018. (code) n Depth Estimation [63] Z. Li, Z. Chen, A. Li, L. Fang, Q. Jiang. X. Liu, J. Jiang*, “Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training,” ECCV 2022. [64] 江俊君，李震宇，刘贤明. 基于深度学习的单目深度估计. 计算机学报, 2022, v.45; No. 473(06): 1276-1301. [65] Z. Li, Z. Chen, X. Liu, and J. Jiang, “DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation,” Machine Intelligence Research, 2023. [66] Z. Li, Z. Chen, A. Li, L. Fang, Q. Jiang, X. Liu, J. Jiang*, B. Zhou, and H. Zhao, “SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations,” accepted to AAAI 2022. [67] J. Xu, X. Liu*, Y. Bai, J. Jiang, K. Wang, X. Chen, X. Ji, "Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation", ACM MM 2022 (CCF-A) n Image Matching [68] Y. Lu, J. Ma, L. Fang, X. Tian, J. Jiang, “Robust and Scalable Gaussian Process Regression and Its Applications Poster Session THU-PM,” CVPR 2023. [69] J. Jiang, Q. Ma, X. Jiang, and J. Ma, “Ranking list preservation for feature matching,” Pattern Recognition, vol. 111, pp. 107665, 2021. [70] J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, “Image Matching from Handcrafted to Deep Features: A Survey,” International Journal of Computer Vision (IJCV), vol. 129, pp. 23–79, 2021. [71] A. Fan, X. Jiang, Y. Wang, J. Jiang, and J. Ma*, “Geometric Estimation via Robust Subspace Recovery,” in ECCV, 2020. [72] X. Jiang, J. Ma, J. Jiang, X. Guo, “Robust Feature Matching Using Spatial Clustering With Heavy Outliers,” IEEE Transactions on Image Processing, vol. 29, pp, 736-746, 2020. [73] J. Ma, X. Jiang, J. Jiang*, J. Zhao, and X. Guo, “LMR: Learning A Two-class Classifier for Mismatch Removal,” IEEE Transactions on Image Processing, vol. 28, no. 8, pp. 4045-4059, 2019. (code) [74] J. Ma, J. Zhao, J. Jiang, H. Zhou, and X. Guo, “Locality Preserving Matching,” International Journal of Computer Vision (IJCV), vol. 127, pp. 512-537, 2019. (code). [75] X. Jiang, J. Jiang, A. Fan, Z. Wang, and J. Ma, “Multiscale Locality and Rank Preservation for Robust Feature Matching of Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6462-6472, 2019. [76] J. Ma, J. Wu, J. Zhao, J. Jiang, H. Zhou, and Q. Z. Sheng, “Nonrigid Point Set Registration with Robust Transformation Learning under Manifold Regularization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 10, pp. 3584-3597, Dec. 2019. (code). [77] J. Ma, J. Jiang, H. Zhou, J. Zhao, and X. Guo, “Guided Locality Preserving Feature Matching for Remote Sensing Image Registration,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4435-4447, 2018. (code) [78] J. Ma, J. Zhao, H. Guo, J. Jiang, H. Zhou, and Y. Gao, “Locality Preserving Matching,” in IJCAI, pp. 4492-4498, 2017. [79] J. Ma, J. Zhao, J. Jiang, and H. Zhou, “Non-Rigid Point Set Registration with Robust Transformation Estimation Under Manifold Regularization,” in AAAI, pp. 4218-4224, 2017. (code) [80] J. Ma, H. Zhou, J. Zhao, Y. Gao, J. Jiang, and J. Tian, “Robust feature matching for remote sensing image registration via locally linear transforming,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 12, pp. 6469-6481, 2015. (code) n Person Re-Identification [81] Z. Wang, J. Jiang, Y. Wu, M. Ye, X. Bai, and Shin'ichi Satoh, “Learning Sparse and Identity-Preserved Hidden Attributes for Person Re-Identification,” IEEE Transactions on Image Processing, vol. 29, pp. 2013-2025, 2020. [82] Z. Wang, J. Jiang*, Y. Yu, and S. Satoh, “Incremental Re-identification by Cross-Direction and Cross-Ranking Adaption,” IEEE Transactions on Multimedia, vol. 21, no. 9, pp. 2376-2386, 2019. [83] Z. Wang, R. Hu, C. Chen, Y. Yu, J. Jiang, C. Liang, and S. Satoh, “Person Reidentification via discrepancy matrix and matrix metric,” IEEE Transactions on Cybernetics, vol. 48, no. 10, 2018. (code) [84] Z. Wang, R. Hu, Y. Yu, J. Jiang, J. Ma, and S. Satoh, “Statistical Inference of Gaussian-Laplace Distribution for Person Verification,” in ACM MM, pp. 1609-1617, 2017. [85] Z. Wang, R. Hu, C. Liang, Y. Yu, J. Jiang, M. Ye, J. Chen, and Q. Leng, “Zero-shot Person Re-identification via Cross-view Consistency,” IEEE Transactions on Multimedia, vol. 18, no. 2, pp. 260-272, 2016. (code) [86] Z. Wang, R. Hu, Y. Yu, J. Jiang, C. Liang and J. Wang, “Scale-adaptive Low-resolution Person Re-identification via Learning A Discriminating Surface,” in IJCAI, pp. 2669-1275, 2016. n Computational Imaging [87] Y. Pan, J. Jiang*, K. Jiang, Z. Wu, K. Yu, and X. Liu, “OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition,” CVPR 2024. [88] F. Zhang, X. Liu, C. Guo, S. Lin, J. Jiang, and X. Ji, “Physics-based Iterative Projection Complex Neural Network for Phase Retrieval”, in CVPR, 2021. [89] Q. Li, X. Liu, J. Jiang, C. Guo, X. Ji, and X. Wu, “Rapid Whole Slide Imaging via Dual-shot Deep Autofocusing,” IEEE Transactions on Computational Imaging, vol.7, pp. 124-136, 2021. n Deraining [90] K. Jiang, J. Jiang*, X. Liu, X. Xu, and X. Ma “FMRNet: Image Deraining via Frequency Mutual Revision,” AAAI 2024. [91] K. Jiang, W. Liu, Z. Wang, X. Zhong, J. Jiang*, C. W. Lin, “DAWN: Direction-aware Attention Wavelet Network for Image Deraining,” Proceedings of the 30th ACM International Conference on Multimedia (ACM MM). 2023. [92] K. Jiang, Z. Wang, Z. Wang, P. Yi, J. Jiang, J. Xiao, C. Lin, “DANet: Image Deraining via Dynamic Association Learning,” IJCAI, 2022. [93] K. Jiang, Z. Wang, Y. Peng. C. Chen, Z. Wang, X. Wang, J. Jiang, and C-W. Lin, “Rain-free and Residue Hand-in-Hand: A Progressive Coupled Network for Real-Time Image Deraining,” IEEE Transactions on Image Processing, vol. 30, pp. 7404-7418, 2021. [94] K. Jiang, Z. Wang, P. Yi, C. Chen, J. Huang, Z. Han, T. Lu, and J. Jiang, “Decomposition Makes Better Rain Removal: An Improved Attention-guided Deraining Network,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 10, pp. 3981–3995, 2021. (code) [95] K. Jiang, Z. Wang, P. Yi, C. Chen, B. Huang, Y. Luo, J. Ma, and J. Jiang, “Multi-Scale Progressive Fusion Network for Single Image Deraining,” in CVPR, Jun. 2020. (paper: arXiv; code) [96] B. Pang, D. Zhai, J. Jiang, and X. Liu, “Single Image Deraining via Scale-space Invariant Attention Neural Network,” in ACM MM, pp. 375-383, 2020. (paper: arXiv; code) n Others (Robust Machine Learning, 3D Point Cloud, Action Recognition, Object Tracking) [97] Jiahan Li, Jiuyang Dong, ShenjinHuang, Xi Li, Junjun Jiang, Xiaopeng Fan, Yongbing Zhang, “Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly-supervised Learning,” CVPR 2024. [98] Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji, “Asymmetric Loss Functions for Noise-tolerant Learning: Theory and Applications,” accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence. [99] Feilong Zhang, Yinchuan Li, Shiyi Lin, Yunfeng shao, Junjun Jiang, Xianming Liu, “Large Sparse Kernels for Federated Learning,” ICLR 2023. [100] Feilong Zhang, Xianming Liu, Shiyi Lin, Gang Wu, Xiong Zhou, Junjun Jiang, Xiangyang Ji, “No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation,” accepted by ICML2023 [101] C. Wang, J. Jiang, X. Zhou, and X. Liu, “ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation,” IEEE Transactions on Neural Networks and Learning Systems, 2022. (Code) [102] Tong Shao, Deming Zhai, Junjun Jiang, Xianming Liu, "Hybrid Conditional Deep Inverse Tone Mapping", ACM Multimedia 2022 (CCF-A) [103] Jinwang Pan, Deming Zhai, Yuanchao Bai, Junjun Jiang, Debin Zhao, Xianming Liu, "ChebyLighter: Optimal Curve Estimation for Low-light Image Enhancement", ACM MM 2022 (CCF-A) [104] Xiong Zhou, Xianming Liu*, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji, "Learning Towards the Largest Margins", ICLR 2022. [105] Yiqi Zhong, Xianming Liu*, Deming Zhai, Junjun Jiang, Xiangyang Ji, "Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon", CVPR 2022（CCF-A） [106] Wenbo Zhao, Xianming Liu*, Zhiwei Zhong, Junjun Jiang, Wei Gao, Ge Li, Xiangyang Ji, "Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation", CVPR 2022（CCF-A） [107] X. Zhou, X. Liu, D. Zhai, J. Jiang, X. Gao, X. Ji, “Prototype-anchored Learning for Learning with Imperfect Annotations,” accepted to ICML, 2022. [108] W. Zhao, X. Liu, J. Jiang, D. Zhao, G. Li, and X. Ji, “Local Surface Descriptor for Geometry and Feature Preserved Mesh Denoising,” accepted to AAAI 2022. [109] Y. Bai, X. Yang, X. Liu, J. Jiang, Y. Wang, X. Ji, and W. Gao, “Towards End-to-End Image Compression and Analysis with Transformers,” accepted to AAAI 2022. [110] X. Zhou, X. Liu, J. Jiang, X. Gao, X. Ji, “Asymmetric Loss Functions for Learning with Noisy Labels,” in ICML, 2021. [111] X. Zhou, X. Liu, C. Wang, D. Zhai, J. Jiang, X. Ji, “Learning with Noisy Labels via Sparse Regularization,” in ICCV, 2021. [112] J. Yan, D. Zhai, J. Jiang, X. Liu, “Target-guided Adaptive Base Class Reweighting for Few-Shot Learning”, in ACM MM 2021. [113] J. Ma, C. Peng, X. Tian, and J. Jiang. “DBDnet: A Deep Boosting Strategy for Image Denoising,” IEEE Transactions on Multimedia, In Press. [114] Z. Xu, R. Hu, J. Chen, C. Chen, J. Jiang, J. Li, and H. Li, “Semi-Supervised Discriminant Multi-Manifold Analysis for Action Recognition,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 10, pp. 2951 - 2962, 2019. [115] W. Ruan, J. Chen, Y. Wu, J. Wang, C. Liang, R. Hu, and J. Jiang, “Multi-Correlation Filters with Triangle-Structure Constraints for Object Tracking,” IEEE Transactions on Multimedia, vol. 21, no. 5, pp. 1122-1134, 2019. [116] C. Chen, M. Liu, B. Zhang, J. Han, J. Jiang and H. Liu, “3D Action Recognition Using Multi-temporal Depth Motion Maps and Fisher Vector,” in IJCAI, New York City, NY, July 2016. Book 名称 江俊君，黄克斌. 基于一致流形学习的人脸超分辨率算法研究 [M]. 科学出版社，2016.