Publications

Publications

Selected Publications

  • Yulong Wang, Hang Su, Xiaolin Hu, Interpret Neural Networks by Identifying Critical Data Routing Paths, in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018. （CCF A, Spotlight）
  • Haosheng Zou, Hang Su, Shihong Song, and Jun Zhu. Understanding Human Behavior in Crowds by Imitating the Decision-Making Process, in 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018. （CCF A）
  • Hang Su, Jun Zhu, Yinpeng Dong, and Bo Zhang. Forecast the Plausible Paths in Crowd Scenes, in International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, 2017 （CCF A）
  • Yinpeng Dong, Hang Su, Jun Zhu, and Bo Zhang. Improving Interpretability of Deep Neural Networks with Semantic Information, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 2017 （CCF A）
  • Hang Su, Jun Zhu, Zhaozheng Yin, Yinpeng Dong, and Bo Zhang. Efficient and Robust Semi-supervised Learning over a Sparse-Regularized Graph, European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, 2016 (CCF B)
  • Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh, Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph , Computer Vision and Pattern Recognition, IEEE Conference on (CVPR), 2015. (CCF A)
  • Hang Su, Zhaozheng Yin, Seungil Huh, Takeo Kanade, Jun Zhu, “Interactive Cell Segmentation based on Active and Semi-supervised Learning,” IEEE Transactions on Medical Imaging (TMI), 2015 
  • Hang Su, Zhaozheng Yin, Seungil Huh, Takeo Kanade, Cell Segmentation in Phase Contrast Microscopy Images via Semi-supervised Classification over Optics-related Features, Medical Image Analysis (MIA), vol. 17, pp. 746-765, Oct. 2013.
  • Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, and Sha Wei, The Large-Scale Crowd Behavior Perception Based on Spatio-Temporal Viscous Fluid Field, Information Forensics and Security, IEEE Transactions on (TIFS), vol.8, pp.1575-1589, Oct. 2013
  • Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh, Phase Contrast Image Restoration via Dictionary Representation of Diffraction Patterns, the 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2012, pp. 615-622. (Young Investigator Award)
  • Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, and Sha Wei, Crowd Event Perception Based On Spatio-Temporal Viscous Fluid Field, International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2012, pp. 458-463. (Best Paper Award)

All Publications

[1]     J Chen, X Yang, H Yin, M Ma, B Chen, J Peng, Y Guo, Z Yin, H Su. AdvFAS: A robust face anti-spoofing framework against adversarial examples,Computer Vision and Image Understanding 235, 103779

[2]     H Chen, F Qian, C Liu, Y Zhang, H Su, S Zhao. Training Robust Deep Collaborative Filtering Models via Adversarial Noise Propagation, ACM Transactions on Information Systems 42 (1), 1-27

[3]     C Chen, Y Guo, F Tian, S Liu, W Yang, Z Wang, J Wu, H Su, H Pfister. Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

[4]     S Ruan, Y Dong, H Su, J Peng, N Chen, X Wei. Towards Viewpoint-Invariant Visual Recognition via Adversarial Training

[5]     V Jampani, H Su, D Sun, MH Yang, J Kautz. NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data, Bilateral convolution layer network for processing point clouds,US Patent 11,636,668

[6]     C Ying, Y Qiaoben, X Zhou, H Su, W Ding, J Ai. Consistent attack: Universal adversarial perturbation on embodied vision navigation, Pattern Recognition Letters 168, 57-63

[7]     W Xiang, H Su, C Liu, Y Guo, S Zheng. Improving the robustness of adversarial attacks using an affine-invariant gradient estimator, Computer Vision and Image Understanding 229, 103647

[8]     L Han, H Su, Z Yin. Phase Contrast Image Restoration by Formulating Its Imaging Principle and Reversing the Formulation With Deep Neural Networks, IEEE Transactions on Medical Imaging 42 (4), 1068-1082

[9]     KY Liu, XY Li, YR Lai, H Su, JC Wang, CX Guo, H Xie, JS Guan, Y Zhou. Denoised internal models: a brain-inspired autoencoder against adversarial attacks, Machine Intelligence Research 19 (5), 456-471

[10]   Y Zhou, H Su, S Tian, X Liu, J Suo. Multiple-kernelized-correlation-filter-based track-before-detect algorithm for tracking weak and extended target in marine radar systems, IEEE Transactions on Aerospace and Electronic Systems 58 (4), 3411-3426

[11]   X Mao, Y Chen, S Wang, H Su, Y He, H Xue. Composite adversarial attacks, Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 8884-8892

[12]   K Wei, J Li, M Ding, C Ma, H Su, B Zhang, HV Poor. User-level privacy-preserving federated learning: Analysis and performance optimization, IEEE Transactions on Mobile Computing 21 (9), 3388-3401

[13]   X Li, J Li, Y Chen, S Ye, Y He, S Wang, H Su, H Xue. Qair: Practical query-efficient black-box attacks for image retrieval, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …

[14]   Z Yin, H Su. Microscopy image formation, restoration, and segmentation, Computer Vision for Microscopy Image Analysis, 13-41

[15]   H Zhao, D Wu, H Su, S Zheng, J Chen. Gradient-based conditional generative adversarial network for non-uniform blind deblurring via DenseResNet, Journal of Visual Communication and Image Representation 74, 102921

[16]   H Su, Z Chang, M Yu, J Gao, X Li, S Zheng. Convolutional neural network with adaptive inferential framework for skeleton-based action recognition, Journal of Visual Communication and Image Representation 73, 102925

[17]   Z Chang, Z Qin, H Fan, H Su, H Yang, S Zheng, H Ling. Weighted bilinear coding over salient body parts for person re-identification, Neurocomputing 407, 454-464

[18]   H Zhao, H Yang, H Su, S Zheng. Natural image deblurring based on ringing artifacts removal via knowledge-driven gradient distribution priors, IEEE Access 8, 129975-129991

[19]   Y Wang, H Su, B Zhang, X Hu. Interpret neural networks by extracting critical subnetworks, IEEE Transactions on Image Processing 29, 6707-6720

[20]   Y Wang, X Zhang, X Hu, B Zhang, H Su. Dynamic network pruning with interpretable layerwise channel selection,Proceedings of the AAAI conference on artificial intelligence 34 (04), 6299-6306

[21]   Y Wang, X Zhang, L Xie, J Zhou, H Su, B Zhang, X Hu. Pruning from scratch, Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 12273 …

[22]   C Chen, J Yuan, Y Lu, Y Liu, H Su, S Yuan, S Liu. Oodanalyzer: Interactive analysis of out-of-distribution samples,IEEE transactions on visualization and computer graphics 27 (7), 3335-3349

[23]   Fan Bao, Chongxuan Li, Kun Xu, Hang Su, Jun Zhu, Bo Zhang. Bi-level Score Matching for Learning Energy-based Latent Variable Models, To Appear in proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

[24]   Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su. Adversarial Distributional Training for Robust Deep Learning, To Appear in proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

[25]   Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Jun Zhu, Hang Su. Boosting Adversarial Training with Hypersphere Embedding, To Appear in proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

[26]   Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu. Benchmarking Adversarial Robustness on Image Classification, To Appear in proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, 2020. (Oral, Accept rate~5%)

[27]   Shiyu Huang, Hang Su, Jun Zhu, and Ting Chen. SVQN: Sequential Variational Soft Q-Learning Networks, To Appear in proc. of International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.

[28]   Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang. Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters, In proc. of European Conference on Computer Vision (ECCV), Online (COVID-19 pandemic), 2020. (Oral, Accept rate~2%)

[29]   Yueru Li, Shuyu Cheng, Hang Su, Jun Zhu. Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds, In proc. of European Conference on Computer Vision (ECCV), Online (COVID-19 pandemic), 2020.

[30]   Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu. Accumulative Poisoning Attacks on Real-time Data, To Appear in proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2021.

[31]   Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu. Bag of Tricks for Adversarial Training, To Appear in proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2021.

[32]   Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu. Black-box Detection of Backdoor Attacks with Limited Information and Data, To Appear in proc. of International Conference on Computer Vision (ICCV), Online (due to COVID-19), 2021.

[33]   "Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, Hui Xue. Towards Face Encryption by Generating Adversarial Identity Masks, To Appear in proc. of International Conference on Computer Vision (ICCV), Online (due to COVID-19), 2021.

[34]   Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu. LiBRe: A Practical Bayesian Approach to Adversarial Detection, To Appear in proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2021.

[35]   Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu. Unsupervised Part Segmentation through Disentangling Appearance and Shape, To Appear in proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2021.

[36]   Haosheng Zou, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu. Learning Task-Distribution Reward Shaping with Meta-Learning, In proc. of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), Online (due to COVID-19), 2021.

[37]   Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong. Triple Memory Networks: a Brain-Inspired Method for Continual Learning, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), in press.

[38]   Shilong Liu, Yaoyuan Liang, Feng Li, Shijia Huang, Hao Zhang, Hang Su, Jun Zhu, Lei Zhang. DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding, The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), Washington DC, USA, 2023.

[39]   Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang, Xingxing Wei, Jun Zhu. On Viewpoint Robustness of Visual Recognition in the Wild, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

[40]   Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng. A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

[41]   Qi-An Fu, Yinpeng Dong, Hang Su, Jun Zhu, Chao Zhang. AutoDA: Automated Decision-based Iterative Adversarial Attacks, To Appear in proc. of 31st USENIX Security Symposium (USENIX Security '22 Winter), Boston, MA, USA, 2022.

[42]   Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang. Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models, To Appear in proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

[43]   Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu. GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing, To Appear in proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

[44]   Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu. Exploring Memorization in Adversarial Training, To Appear in proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2022.

[45]   Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang. DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR, To Appear in proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2022.

[46]   Jialian Li, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu. Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model, To Appear in proc. of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), Online (due to COVID-19), 2022.

[47]   Yinpeng Dong, Shuyu Cheng, Tianyu Pang, Hang Su, Jun Zhu. Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), in press, 2022.

[48]   Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, Jun Zhu. Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors, In proc. of International Joint Conference on Artificial Intelligence (IJCAI), Online (due to COVID-19), 2022. (Long Oral, Accept rate~3.8%)

[49]   ChengYang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu. Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk, In proc. of International Joint Conference on Artificial Intelligence (IJCAI), Online (due to COVID-19), 2022.

[50]   ChengYang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu. Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk, In proc. of International Joint Conference on Artificial Intelligence (IJCAI), Online (due to COVID-19), 2022.

[51]   Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Hang Su, Jun Zhu. Tianshou: A Highly Modularized Deep ReinforcementLearning Library, Journal of Machine Learning Research, in press, 2022. (Github with 5k+ stars: TianShou)

[52]   Fan Bao, Shen Nie, Kaiwen Xue, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu. One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

[53]   Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu. Exact Energy-Guided Diffusion Sampling via Contrastive Energy Prediction, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023

[54]   Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu. NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

[55]   Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu. MultiAdam: Parameter-wise Scale-invariant Optimizer for Physics-informed Neural Network, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

[56]   Zhongkai Hao, Chengyang Ying, Zhengyi Wang, Hang Su, Yinpeng Dong, Songming Liu, Ze Cheng, Jun Zhu, Jian Song. GNOT: A General Neural Operator Transformer for Operator Learning, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023

[57]   Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu. On the Reuse Bias in Off-Policy Reinforcement Learning, In proc. of International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, 2023.

[58]   Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu. Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling, In proc. of International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.

[59]   Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, Heung-Yeung Shum. DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection, In proc. of International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.

[60]   Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng. Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients, In proc. of International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.

[61]   Shilong Liu, Shijia Huang, Feng Li, Hao Zhang, Yaoyuan Liang, Hang Su, Jun Zhu, Lei Zhang. DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding, In proc. of AAAI Conference on Artificial Intelligence (AAAI), Washington DC, USA, 2023.

[62]   Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu. All are Worth Words: A ViT Backbone for Diffusion Models, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver Canada, 2023.

[63]   Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu. Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver Canada, 2023.

[64]   Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu. Benchmarking Robustness of 3D Object Detection to Common Corruptions, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver Canada, 2023


Earlier Publication

  • Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, 2019. (CCF A, Oral)
  • Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. Efficient Decision-based Black-box Adversarial Attacks on Face Recognition, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, 2019 (CCF A)
  • Xingxing Wei, Jun Zhu, Hang Su and Sha Yuan. Sparse Adversarial Perturbations for Videos, To appear in the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, USA, 2019.
  • Shiyu Huang, Hang Su, Jun Zhu, and Tim Chen. Combo-Action: Training Agent For FPS Game with Auxiliary Tasks, To appear in the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, USA, 2019.
  • Dong Yan，Shiyu Huang, Hang Su, Jun Zhu, Learning to Assign Credit in Reinforcement Learning by Incorporating Abstract Relations, in AAAI-19 Workshop on Reinforcement Learning in Games, 2019
  • Yinpeng Dong, Hang Su, Jun Zhu, Fan Bao, and Bo Zhang, Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples, AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019
  • Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Hang Su, and Jun Zhu. Stochastic Quantization for Learning Accurate Low-bit DeepNeural Networks, International Journal of Computer Vision (IJCV), in press, 2019.
  • Yulong Wang, Hang Su, Xiaolin Hu, Interpret Neural Networks by Identifying Critical Data Routing Paths,  in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018. （CCF A, Spotlight）
  • Juzheng Li, Hang Su, Jun Zhu, Siyu Wang, and Bo Zhang. Textbook Question Answering under Teacher Guidance with Memory Networks, in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018 （CCF A, Spotlight）
  • Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting Adversarial Attacks with Momentum, in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018. (We won the first places in both " Non-targeted Adversarial Attack" and " Targeted Adversarial Attacks" of NIPS 2017 Adversarial Attacks and Defenses) （CCF A, Spotlight）
  • Haosheng Zou, Hang Su, Shihong Song, and Jun Zhu. Understanding Human Behavior in Crowds by Imitating the Decision-Making Process,  in 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018. （CCF A）
  • Qin Zhou, Heng Fan, Shibao Zheng, Hang Su, Xinzhe Li, Shuang Wu, Haibin Ling,  Graph Correspondence Transfer for Person Re-identification, in 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018 (Oral) （CCF A）
  • Juzheng Li, Hang Su, Jun Zhu, Bo Zhang, Essay-Anchor Attentive Multi-Modal Bilinear Pooling for Textbook Question Answering,  in2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, USA, 2018 （CCF B, Oral）
  • Xiao Yang, Hang Su, Qin Zhou, Xinzhe Li, Shibao Zheng,  Recognizing Minimal Facial Sketch by Generating Photorealistic Faces with the Guidance of Descriptive Attributes, in International Conference on Acoustics, Speech, and Signal Processing (ICASSP)， 2018 （CCF B）
  • Danyang Sun, Tongzheng Ren, Chongxun Li, Jun Zhu, Hang Su, Learning to Write Stylized Chinese Characters by Reading a Handful of Examples, in International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 2018 （CCF A）
  • Mengchen Liu, Shixia Liu, Hang Su, Kelei Cao, and Jun Zhu. Analyzing the Noise Robustness of Deep Neural Networks, IEEE Conference on Visual Analytics Science and Technology (VAST), Berlin, Germany, 2018 (CCF A)
  • Xingxing Wei, Jun Zhu, Sitong Feng, and Hang Su. Video-to-Video Translation with Global Temporal Consistency, In Proc. of ACM Multimedia (MM), Seoul, Korea, 2018.
  • Hang Su, Jun Zhu, Yinpeng Dong, and Bo Zhang. Forecast the Plausible Paths in Crowd Scenes,  in International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, 2017 （CCF A）
  • Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, and Bo Zhang. Semi-supervised Max-margin Topic Models with Manifold Posterior Regularization,  in International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, 2017 （CCF A）
  • Yinpeng Dong, Hang Su, Jun Zhu, and Bo Zhang. Improving Interpretability of Deep Neural Networks with Semantic Information, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 2017 （CCF A）
  • Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Jun Zhu, and Hang Su. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization,  in the 28th British Machine Vision Conference (BMVC), London, 2017 (Oral, Best Paper Nomination)
  • Shuang Wu, Shibao Zheng, Hua Yang, Hang Su, Yawen Fan, M.H. Yang. Crowd Behavior Analysis via Curl and Divergence of Motion Trajectories, International Journal of Computer Vision (IJCV), 2017
  • Qin Zhou, Shibao Zheng, Haibin Ling, Hang Su, Shuang Wu, Joint Dictionary and Metric Learning for Person Re-identification, Pattern Recognition （PR）, 2017
  • Shuang Wu, Hua Yang, Shibao Zhen, Hang Su, Qin Zhou, Xu Lu. Motion Sketch based Crowd Video Retrieval, Multimedia Tools and Applications （MTA）, 2017
  • Shuang Wu, Hang Su, Hua Yang, Shiabao Zheng, Yawen Fan, Qin Zhou. Bilinear Dynamics for Crowd Video Analysis, Journal of Visual Communication and Image Representation （JVCIR），2017
  • Hang Su, Jun Zhu, Zhaozheng Yin, Yinpeng Dong, and Bo Zhang. Efficient and Robust Semi-supervised Learning over a Sparse-Regularized Graph, European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, 2016 (CCF B)
  • Kun Xu, Hang Su, Jun Zhu, Ji-Song Guan, and Bo Zhang. Neuron Segmentation Based on CNN With Semi-Supervised Regularization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 20-28. 2016.
  • Shuang Wu, Hang Su, Shibao Zheng, Hua Yang, Qin Zhou, Motion Sketch Based Crowd Video Retrieval via Motion Structure Coding, International Conference on Image Processing (ICIP), 2016 (CCF C)
  • Qin Zhou, Shibao Zheng, Hua Yang, Yu Wang and Hang Su, Joint Instance and Feature Importance Re-weighting for Person Reidentification, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 2016, pp. 1546-1550. (CCF B)
  • Hua Yang, Xinyu Wang, Ji Zhu, Wenqi Ma, and Hang Su. Resolution adaptive feature extracting and fusing framework for person re-identification. Neurocomputing, 212 (2016): 65-74.
  • Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh, Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph , Computer Vision and Pattern Recognition, IEEE Conference on (CVPR), 2015. (CCF A)
  • Hang Su, Zhaozheng Yin, Seungil Huh, Takeo Kanade, Jun Zhu, “Interactive Cell Segmentation based on Active and Semi-supervised Learning,” IEEE Transactions on Medical Imaging (TMI), 2015
  • Hang Su, Hua Yang, Shibao Zheng, Sha Wei, Shuang Wu, Towards Active Annotation for Detection of Numerous and Scattered Objects , Multimedia and Expo, IEEE International Conference on (ICME), 2015. (CCF B)
  • Zhaozheng Yin, Hang Su, Elmer Ker, Mingzhong Li, Haohan Li, Cell Sensitive Phase Contrast Microscopy Imaging by Multiple Exposures, Medical Image Analysis (MIA), 2015
  • Sha Wei, Jun Li, Wen Chen, Lizhong Zheng, and Hang Su, Design of Generalized Analog Network Coding for a Multiple-Access Relay Channel, IEEE Transactions on Communications (TCOM), vol. 63, no. 1, pp. 170-185, Jan. 2015.
  • Qin Zhou, Shibao Zheng, Hang Su, Kernelized View Adaptive Subspace Learning for Person Re-identification, The British Machine Vision Conference (BMVC), 2015 (CCF C)
  • Hua Yang, Xinyu Wang, Wenqi Ma, Hang Su, Ji Zhu, Multiple Scaled Person Re-Identification Framework for HD Video Surveillance Application, Computer Vision of China (CCCV), 2015, pp. 219-228
  • Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh, Interactive Cell Segmentation based on Correction Propagation, International Symposium on Biomedical Imaging (ISBI), 2014. 
  • Y. Zhou, Weiyao Lin, Hang Su, J. Wu, J. Wang, Y. Zhou, Representing and Recognizing Motion Trajectories: A Tube and Droplet Approach, ACM Multimedia (MM), 2014. (CCF A)
  • Hang Su, Zhou Su, Shibao Zheng, Hua Yang, and Sha Wei. Interactive cell segmentation based on phase contrast optics. Bio-medical materials and engineering 24, no. 1 (2014): 29-35.
  • Zhaozheng Yin, Hang Su, Elmer Ker, Mingzhong Li, and Haohan Li, Cell Sensitive Microscopy Imaging for Cell Image Segmentation, the 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2014 
  • Shuang Wu, Shibao Zheng, Hua Yang, Yawen Fan, Longfei Liang, Hang Su, SAGTA: Semi-Automatic Ground Truth Annotation in Crowd Scene, Multimedia and Expo, IEEE International Conference on (ICME), 2014 
  • Sha Wei, Jun Li, Wen Chen, Hang Su, Zihuai Lin, and Branka Vucetic, Power Adaptive Network Coding for a Non-orthogonal Multiple-Access Relay Channel, Communication, IEEE Transactions on (TCOM), 2014
  • Changyou Deng, Hua Yang, and Hang Su. Human Detection based on CENTRIST and Scale of Edge Selection. In Broadband Multimedia Systems and Broadcasting (BMSB), 2014 IEEE International Symposium on, pp. 1-5. IEEE, 2014.
  • Xiaowei Lu, Hua Yang, Hang Su, and Shuang Wu. Flux based Pedestrian Flow Estimation in Occluded Scenes. In Broadband Multimedia Systems and Broadcasting (BMSB), 2014 IEEE International Symposium on, pp. 1-5. IEEE, 2014.
  • Hang Su, Zhaozheng Yin, Seungil Huh, Takeo Kanade, Cell Segmentation in Phase Contrast Microscopy Images via Semi-supervised Classification over Optics-related Features, Medical Image Analysis (MIA), vol. 17, pp. 746-765, Oct. 2013. 
  • Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, and Sha Wei, The Large-Scale Crowd Behavior Perception Based on Spatio-Temporal Viscous Fluid Field, Information Forensics and Security, IEEE Transactions on (TIFS), vol.8, pp.1575-1589, Oct. 2013
  • Hang Su, Zhaozheng Yin, Seungil Huh, Takeo Kanade, Cell Segmentation Via Spectral Analysis on Phase Retardation Features, International Symposium on Biomedical Imaging (ISBI), 2013, pp. 1469-1475. 
  • Seungil Huh, Hang Su, Mei Chen, and Takeo Kanade, Efficient Phase Contrast Microscopy Restoration Applied for Muscle Myotube Detection, the 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2013, pp. 420-427. 
  • Haiyan Yin, Hua Yang, Hang Su, Chongyang Zhang, Dynamic Background Subtraction Based on Appearance and Motion Pattern, Multimedia and Expo Workshops, IEEE International Conference on (ICMEW), 2013.
  • Yawen Fan, Hua Yang, Shibao Zheng, Hang Su, Shuang Wu, Video Sensor based Complex Scene Analysis with Granger Causality,  Sensors, 2013 
  • Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh, Phase Contrast Image Restoration via Dictionary Representation of Diffraction Patterns, the 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2012, pp. 615-622. (Young Investigator Award) 
  • Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, and Sha Wei, Crowd Event Perception Based On Spatio-Temporal Viscous Fluid Field, International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2012, pp. 458-463. (Best Paper Award) 
  • Seungil Huh, Hang Su, Takeo Kanade, Apoptosis Detection for Adherent Cell Populations in Time-lapse Phase-contrast Microscopy Images, the 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2012: 331-339. 
  • Sha Wei, Jun Li, Wen Chen, and Hang Su, Wireless Adaptive Network Coding Strategy in Multiple-Access Relay Channels. IEEE International Conference on Communications (ICC), 2012, pp.751-755. 
  • Hua yang, Yihua cao, Hang Su, The Large-scale Crowd Analysis Based on Sparse Spatial-temporal Local Binary Pattern, Multimedia Tools and Applications (MTA), Oct. 2012. 
  • Yawen Fan, Shibao Zheng, Hua Yang, Chongyang Zhang, Hang Su, Causality Weighted Active Learning for Abnormal Event Identification based on the Topic Model, Optical Engineering (OE), vol. 51, 2012. 
  • Hua Yang, Hang Su, Shibao Zheng, Sha Wei, and Yawen Fan, The Large-scale Crowd Density Estimation based on Sparse Spatiotemporal Local Binary Pattern, Multimedia and Expo, 2011 IEEE International Conference on (ICME), 2011, pp.1-6. 
  • Ling Li, Hua Yang, Hang Su, Yihua Cao, and Shibao Zheng, A Web-Based HD Telemedicine System for Remote Psychotherapy, Cyber-Enabled Distributed Computing and Knowledge Discovery, International Conference on (CyberC), 2011, pp.544-550. 
  • Yawen Fan, Hua Yang, Shibao Zheng and Hang Su, Geometric Motion Flow (GMF): A New Feature for Traffic Surveillance, Image and Graphics, 2011 Sixth International Conference on (ICIG), 2011, pp.726-730. 
  • Hang Su, Yang Hua and Zheng Shibaozheng, The Large-scale Crowd Density Estimation based on Region Feature Extraction Method, Asian Conference on Computer Vision (ACCV), 2010, pp.1605-1616.