publications-2

IJCAI 2020 Tutorial on Trustworthiness of Interpretable Machine Learning
[Website] [Video]
PRCV 2020 Tutorial on Robust and Explainable Artificial Intelligence

Direction 1: Interpretability of Neural Networks

Conference papers

Interpreting Multivariate Interactions in DNNs
Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang
AAAI, 2021
[PDF]

Building Interpretable Interaction Trees for Deep NLP Models
Die Zhang, Huilin Zhou, Hao Zhang, Xiaoyi Bao, Da Huo, Ruizhao Chen, Xu Cheng, Mengyue Wu, and Quanshi Zhang
AAAI, 2021
[PDF]

3D-Rotation-Equivariant Quaternion Neural Networks
Wen Shen, Binbin Zhang, Shikun Huang, Zhihua Wei, and Quanshi Zhang
ECCV, 2020
[PDF] [Code]

Interpreting Multivariate Interactions in DNNs
Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, and Quanshi Zhang
AAAI 2021
[PDF]
Building Interpretable Interaction Trees for Deep NLP Models
Die Zhang, Huilin Zhou, Hao Zhang, Xiaoyi Bao, Da Huo, Ruizhao Chen, Xu Cheng, Mengyue Wu, and Quanshi Zhang
AAAI 2021
[PDF]
3D-Rotation-Equivariant Quaternion Neural Networks
Wen Shen, Binbin Zhang, Shikun Huang, Zhihua Wei, and Quanshi Zhang
ECCV 2020
[PDF]
Explaining Knowledge Distillation by Quantifying the Knowledge
Xu Cheng, Zhefan Rao, Yilan Chen, and Quanshi Zhang
CVPR 2020
[PDF]
Knowledge Consistency between Neural Networks and Beyond
Ruofan Liang, Tianlin Li, Longfei Li, Jing Wang, and Quanshi Zhang
ICLR 2020
Interpretable Complex-Valued Neural Networks for Privacy Protection
Liyao Xiang, Hao Zhang, Haotian Ma, Yifan Zhang, Jie Ren, and Quanshi Zhang
ICLR 2020
Explaining Neural Networks Semantically and Quantitatively
Runjin Chen, Hao Chen, Jie Ren, Ge Huang, Quanshi Zhang
ICCV, 2019 (Oral)
[PDF]
Towards A Deep and Unified Understanding of Deep Neural Models in NLP
Chaoyu Guan, Xiting Wang, Quanshi Zhang (Corresponding author), Runjin Chen, Di He, Xing Xie
ICML, 2019
[PDF] [Code]
Interpreting CNNs via Decision Trees
Quanshi Zhang, Yu Yang, Haotian Ma, and Ying Nian Wu
CVPR, 2019
[PDF]  [机器之心]
Unsupervised Learning of Neural Networks to Explain Neural Networks
Quanshi Zhang, Yu Yang, Yuchen Liu, Ying Nian Wu, and Song-Chun Zhu
Full paper in arXiv:1805.07468, 2018
Extended abstract in AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019
[PDF]
Network Transplanting
Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu, and Song-Chun Zhu
Full paper in arXiv:1804.10272, 2018
Extended abstract in AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019
[PDF]

Interpretable Convolutional Neural Networks
Quanshi Zhang
, Ying Nian Wu, and Song-Chun Zhu
CVPR (Spotlight) 2018
[PDF]  [Code]  [Project website]

Interpreting CNN Knowledge via an Explanatory Graph
Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, and Song-Chun Zhu
AAAI, 2018
[PDF]  [Code]  [Project website]  [Video]

Examining CNN Representations with respect to Dataset Bias
Quanshi Zhang, Wenguan Wang, and Song-Chun Zhu

AAAI, 2018
[PDF]
Mining Object Parts from CNNs via Active Question-Answering
Quanshi Zhang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu

CVPR, 2017
[Project website]  [PDF]  [Video]
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning
Quanshi Zhang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu
AAAI, 2017
[PDF]  [Code]  [Project website]  [Video]
Journal papers
Interpretable CNNs for Object Classification
Quanshi Zhang
, Xin Wang, Ying Nian Wu, Huilin Zhou, and Song-Chun Zhu

IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2020 (online available)
[PDF]  [Code]  [Project website]
Extraction of an Explanatory Graph to Interpret a CNN
Quanshi Zhang
, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, and Song-Chun Zhu

IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2020 (online available)
[PDF]  [Code]  [Project website]  [Video]
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering
Quanshi Zhang, Jie Ren, Ge Huang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2020 (online available)
[PDF]
Visual interpretability for Deep Learning: a Survey
Quanshi Zhang and Song-Chun Zhu
Frontiers of Information Technology & Electronic Engineering. Vol. 19, No. 1, page 27-39, 2018
[PDF]  [Project website]  [机器之心]
arXiv papers
Interpreting and Boosting Dropout from a Game-Theoretic View
Hao Zhang, Sen Li, Yinchao Ma, Mingjie Li, Yichen Xie, Quanshi Zhang

arXiv:2009.11729, 2020
[PDF]
A Unified Approach to Interpreting and Boosting Adversarial Transferability
Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang
arXiv:2010.04055, 2020
[PDF]
Interpreting and Disentangling Feature Components of Various Complexity from DNNs Jie Ren, Mingjie Li, Zexu Liu, and Quanshi Zhang
arXiv:2006.15920, 2020
[PDF]
Rotation-Equivariant Neural Networks for Privacy Protection
Hao Zhang, Yiting Chen, Haotian Ma, Xu Cheng, Qihan Ren, Liyao Xiang, Jie Shi, and Quanshi Zhang
arXiv:2006.13016, 2020
[PDF]
Deep Quaternion Features for Privacy Protection
Hao Zhang, Yiting Chen, Liyao Xiang, Haotian Ma, Jie Shi, and Quanshi Zhang
arXiv:2003.08365, 2020
[PDF]
Towards a Unified Evaluation of Explanation Methods without Ground Truth
Hao Zhang, Jiayi Chen, Haotian Xue, and Quanshi Zhang
arXiv:1911.09017, 2019
[PDF]
Utility Analysis of Network Architectures for 3D Point Cloud Processing
Shikun Huang, Binbin Zhang, Wen Shen, Zhihua Wei, and Quanshi Zhang
arXiv:1911.09053, 2019
[PDF]
Quantifying Layerwise Information Discarding of Neural Networks
Haotian Ma, Yinqing Zhang, Fan Zhou, and Quanshi Zhang
arXiv:1906.04109, 2019
[PDF]
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu1, Song-Chun Zhu, and Quanshi Zhang
arXiv:1901.02184, 2019
[PDF]
Interactively Transferring CNN Patterns for Part Localization
Quanshi Zhang, Ruiming Cao, Shengming Zhang, Ying Nian Wu, and Song-Chun Zhu
arXiv:1708.01783, 2017
[PDF]

Direction 2: Graph mining & graph matching

Journal papers

Visual Graph Mining for Graph Matching
Quanshi Zhang, Xuan Song, Yu Yang, Haotian Ma, Ryosuke Shibasaki
Computer Vision and Image Understanding, vol. 178, page 16-29, 2019
[PDF]
Mining Deep And-Or Object Structures via Cost-Sensitive Question-Answer-Based Active Annotations
Quanshi Zhang, Ying Nian Wu, Hao Zhang, and Song-Chun Zhu
Computer Vision and Image Understanding, vol. 176-177, page 33-44, 2018
[PDF]
Object Discovery: Soft Attributed Graph Mining
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 38(3):532-545, 2016
[PDF]  [Project website]  [Code]
From RGB-D Images to RGB Images: Single Labeling for Mining Visual Models
Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao
ACM Transactions on Intelligent Systems and Technology (ACM-TIST), 6(2): 16, 2015
[PDF]
Conference papers
Mining And-Or Graphs for Graph Matching and Object Discovery
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu
ICCV, 2015
Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
CVPR, 2014
Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
ICCV 2013
[PDF]
Category Modeling from just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
CVPR, 2013
[PDF]  [Video]

Direction 3: Other studies

Journal papers
Prediction and Simulation of Human Mobility Following Natural Disasters
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, and Xing Xie
ACM Transactions on Intelligent Systems and Technology (ACM-TIST), 8(2): 29, 2016
[PDF]
Unsupervised Skeleton Extraction and Motion Capture from 3D Deformable Matching
Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, and Huijing Zhao
Neurocomputing, Elsevier, pp.170-182, 2013
[PDF]  [Video]
Intelligent System for Human Behavior Analysis and Reasoning Following Large‐scale Disasters
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, and Ryosuke Shibasaki
IEEE Intelligent Systems, vol. 28, no. 4, pp. 35-42, July-Aug. 2013
[PDF]
A Fully Online and Unsupervised System for Large and High Density Area Surveillance: Tracking, Semantic Scene Learning and Abnormality Detection
Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, Jinshi Cui, and Hongbin Zha
ACM Transactions on Intelligent Systems and Technology (ACMTIST), 4(2): 20, 2013
[PDF]
A Novel Dynamic Model for Multiple Pedestrians Tracking in Extremely Crowded Scenarios
Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, and Hongbin Zha
Information Fusion, Elsevier, 2012
[PDF]
Conference papers
A Simulator of Human Emergency Mobility following Disasters: Knowledge Transfer from Big Disaster Data
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, and Xing Xie
AAAI, 2015
[PDF]
When 3D Reconstruction Meets Ubiquitous RGB-D Images
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
CVPR, 2014
Start from Minimum Labeling: Learning of 3D Object Models and Point Labeling from a Large and Complex Environment
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
ICRA, 2014
[PDF]  [Video]
Prediction of Human Emergency Behavior and their Mobility following Large-scale Disaster
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki
KDD, 2014
[PDF]
Intelligent System for Urban Emergency Management During Large-scale Disaster
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki
AAAI, 2014
[PDF]
Unsupervised 3D Category Discovery and Point Labeling from a Large Urban Environment
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
ICRA, 2013
[PDF]  [Video]  [PPT]
Modeling and Probabilistic Reasoning of Population Evacuation During Large-scale Disaster
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, and Ryosuke Shibasaki
KDD, 2013
[PDF]
Laser-based Intelligent Surveillance and Abnormality Detection in Extremely Crowded Scenarios
Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, and Hongbin Zha
ICRA, 2012
[PDF]
Moving Object Classification using Horizontal Laser Scan Data
Huijing Zhao, Quanshi Zhang, Masaki Chiba, Ryosuke Shibasaki, Jinshi Cui, and Hongbin Zha
ICRA, 2009
[PDF]