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Image Super-Resolution
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.
C. Wang, J. Jiang*, Z. Zhong, and X. Liu, “Spatial-Frequency Mutual Learning for Face Super-Resolution,” CVPR 2023. (Code)
Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Deming Zhai, Xianming 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.
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)
Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Xiangyang Ji, "Guided Depth Map Super-resolution: A Survey", vo. 55, no. 14, ACM Computing Surveys, 2023.
Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Xiangyang Ji, “Deep Attentional Guided Image Filtering,” IEEE Transactions on Neural Networks and Learning Systems.
江俊君，程豪，李震宇，刘贤明，王中元. 深度学习视频超分辨率技术概述. 中国图象图形学报.
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. (paper)
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.
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.
P. Yi, Z. Wang, K. Jiang, J. Jiang, T. Lu, X. Tian, and J. Ma, “Omniscient Video Super-Resolution,” ICCV 2021.
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.
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)
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.
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)
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)
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)
K. Jiang, Z. Wang, P. Yi, and J. Jiang, “Hierarchical Dense Recursive Network for Image Super-Resolution,” Pattern Recognition, vol. 107, pp. 107475, 2020.
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.
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)
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)
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)
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)
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%).
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)
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)
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)
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)
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)
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)
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)
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)
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)
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.
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)
Image Fusion
X. Hu, J. Jiang*. X. Liu .J. Ma, “ZMFF: Zero-Shot Multi-Focus Image Fusion,” Information Fusion. (Code)
邱德粉，胡星宇，梁鹏伟，刘贤明，江俊君*. 红外与可见光图像渐进融合深度网络. 中国图象图形学报.
邱德粉，江俊君*，胡星宇，刘贤明，马佳义. 引导Transformer网络的可见光图像指导红外图像超分辨率方法. 中国图象图形学报.
P. Liang. J. Jiang*, X. Liu .J. Ma, “Fusion from Decomposition: A Self-Supervised Decomposition Approach for lmage Fusion,” ECCV2022【计算机视觉三大顶会之一】
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.
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.
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)
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).
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)
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.
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)
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).
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).
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)
Hyperspectral Image Processing and Analysis
Chen Ma, Junjun Jiang*, Huayi Li*, Wenxue Cui, and Guoyuan Li, “Progressive Token Reduction and Compensation for Hyperspectral Image Representation,” IEEE Transactions on Geoscience and Remote Sensing, 61, pp. 5502814, 2023.
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.
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)
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)
Xinya Wang, Yingsong Cheng, Xiaoguang Mei, Junjun Jiang, and Jiayi Ma, “Group Shuffle and Spectral-Spatial Fusion for Hyperspectral Image Super-Resolution,” IEEE Transactions on Computational Imaging, 8, pp. 1223-1236, Dec. 2022.
Xinya Wang, Qian Hu, Junjun Jiang, and Jiayi 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.
Q. Ma, J. Jiang*, X. Liu, and J. Ma, “Learning A 3D-CNN and Transformer Prior for hyperspectral Image Super-Resolution,” arXiv:2111.13923. (paper)
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.
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.
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)
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.
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)
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)
J. Jiang, C. Chen, Y. Yu, X. Jiang, and J. Ma, “Spatial-Aware Collaborative Representation for Hyperspectral Remote Sensing Image Classification,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 3, pp. 404-408, 2017. (code)
Depth Estimation
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,” ECCV2022【计算机视觉三大顶会之—】
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.
Z. Li, X. Wang, X. Liu, and J. Jiang*, “BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation,” arXiv preprint arXiv:2204.00987, 2022.
Z. Li, Z. Chen, X. Liu, and J. Jiang, “DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation,” arXiv preprint arXiv:2203.14211, 2022.
Z. Li, Z. Chen, A. Li, L. Fang, Q. Jiang, X. Liu, and J. Jiang*, “Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training,” arXiv preprint arXiv:2204.11590.
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.
Z. Li, J. Jiang*, and X. Liu, “Self-supervised Monocular Depth Estimation via Discrete Strategy and Uncertainty,” IEEE/CAA Journal of Automatica Sinica Symposium, 2022.
Jialei Xu, Xianming Liu*, Yuanchao Bai, Junjun Jiang, Kaixuan Wang, Xiaozhi Chen, Xiangyang Ji, "Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation", ACM Multimedia 2022 (CCF-A)
Image Matching
Yifan Lu, Jiayi Ma, Leyuan Fang, Xin Tian, Junjun Jiang, “Robust and Scalable Gaussian Process Regression and Its Applications Poster Session THU-PM,” CVPR 2023.
J. Jiang, Q. Ma, X. Jiang, and J. Ma, “Ranking list preservation for feature matching,” Pattern Recognition, vol. 111, pp. 107665, 2021.
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.
A. Fan, X. Jiang, Y. Wang, J. Jiang, and J. Ma*, “Geometric Estimation via Robust Subspace Recovery,” in ECCV, 2020.
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.
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)
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).
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.
J. Ma, X. Jiang, J. Jiang, and Y. Gao, “Feature-guided Gaussian mixture model for image matching,” Pattern Recognition, vol. 92, pp. 231-245, 2019.
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).
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)
J. Ma, J. Zhao, H. Guo, J. Jiang, H. Zhou, and Y. Gao, “Locality Preserving Matching,” in IJCAI, pp. 4492-4498, 2017.
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)
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)
Person Re-Identification
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.
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.
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)
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.
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)
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.
Computational Imaging
李强，刘贤明，韩凯歌，江俊君，季向阳，“基于深度学习的数字病理扫描系统单次曝光自动对焦方法”，中国科学: 信息科学（CCF-A类中文期刊）
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.
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.
Q. Li, X. Liu, K. Han, C. Guo, J. Jiang, X. Ji, and X. Wu, “Learning to Autofocus in Whole Slide Imaging via Physics-guided Deep Cascade Networks,” Optics Express, In Press.
Deraining
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 (ACMMM). 2023.
K. Jiang, Z. Wang, Z. Wang, P. Yi, J. Jiang, J. Xiao, C. Lin, “DANet: Image Deraining via Dynamic Association Learning,” IJCAI, 2022.
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.
K. Jiang, Z. Wang, Y. Peng. C. Chen, X. Wang, J. Jiang, and Z. Xiong, “Multi-level Memory Compensation Network for Rain Removal via Divide-and-Conquer Strategy,” Journal of Selected Topics in Signal Processing, vol. 15, no. 2, pp. 216 - 228, 2021.
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)
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)
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)
Others (Robust Machine Learning, 3D Point Cloud, Action Recognition, Object Tracking)
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.
Feilong Zhang, Yinchuan Li, Shiyi Lin, Yunfeng shao, Junjun Jiang, Xianming Liu, “Large Sparse Kernels for Federated Learning,” ICLR 2023.
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
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)
Tong Shao, Deming Zhai, Junjun Jiang, Xianming Liu, "Hybrid Conditional Deep Inverse Tone Mapping", ACM Multimedia 2022 (CCF-A)
Jinwang Pan, Deming Zhai, Yuanchao Bai, Junjun Jiang, Debin Zhao, Xianming Liu, "ChebyLighter: Optimal Curve Estimation for Low-light Image Enhancement", ACM Multimedia 2022 (CCF-A)
Xiong Zhou, Xianming Liu*, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji, "Learning Towards the Largest Margins", ICLR2022 【机器学习三大顶会之一】
Yiqi Zhong, Xianming Liu*, Deming Zhai, Junjun Jiang, Xiangyang Ji, "Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon", CVPR2022（CCF-A）
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", CVPR2022（CCF-A）
X. Zhou, X. Liu, D. Zhai, J. Jiang, X. Gao, X. Ji, “Prototype-anchored Learning for Learning with Imperfect Annotations,” accepted to ICML, 2022.
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.
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.
X. Zhou, X. Liu, J. Jiang, X. Gao, X. Ji, “Asymmetric Loss Functions for Learning with Noisy Labels,” in ICML, 2021.
X. Zhou, X. Liu, C. Wang, D. Zhai, J. Jiang, X. Ji, “Learning with Noisy Labels via Sparse Regularization,” in ICCV, 2021.
J. Yan, D. Zhai, J. Jiang, X. Liu, “Target-guided Adaptive Base Class Reweighting for Few-Shot Learning”, in ACM MM 2021.
J. Ma, C. Peng, X. Tian, and J. Jiang. “DBDnet: A Deep Boosting Strategy for Image Denoising,” IEEE Transactions on Multimedia, In Press.
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.
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.
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.
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