Xiang Xiang

Currently Associate Professor at Huazhong University of Science and Technology (HUST, '20-)

Founding Director of HAIV Lab at HUST; PhD in Computer Science at Johns Hopkins University (JHU, '18)

Previously Applied Scientist at Amazon.com, Inc. (AWS AI, Amazon Textract & Amazon Rekognition, '18-20)

Welcome to Xiang's personal homepage. All content on this homepage is the sole responsibility of Xiang and does not reflect the opinions of Xiang's previous or current employer.

News

[11/21] Organizing the HUST Vision and Learning salon (VisaLon 2021).

[10/19] Serving in the program committee of ACM Multimedia 2019.

[6/19] Attending CVPR at Long Beach.

[5/19] Textact project that I participated in is launched for General Availability.

[6/18] Attending CVPR at SLC.

[5/18] Serving in the program committee of the 4th DLMIA at MICCAI'18.

[4/18] Serving in the program committee of the 8th workshop on AMFG  at CVPR'18.

[3/18] Attending the PhD Forum of WACV'18 at Lake Tahoe awarded with the travel grant.

[10/17] Attending ACM Multimedia at Bay Area.

[7/17] Serving as a committee member of the 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA) at MICCAI'17.

[5/17] Giving a talk at MACV'17 hold at UPenn. 

[3/17] Serving as a technical staff of the PASCAL in Detail Challenge at CVPR'17.

[2/17] Our team (JHU) won two 2nd Place awards in the facial expression recognition track and AU detection track in the EmotionNet 2017 Challenge.

Graduated Students

Zihan Zhang (2020-2024, supervised), incoming researcher at China Mobile Research Institute (CMRI). 

Yuwen Tan (2020-2024, supervised), incoming PhD student at Boston University (BU).

Xin Luo (2021, co-advised), now algorithm engineer at Huawei Wuhan Research Institute (HWRI).

Xin Wei (2020-2021, co-advised), now PhD student at University of Wisconsin - Madison (WISC).

Yang Xiang (2020, co-advised), now senior SDE at Microsoft (Redmond) Office AI. 

Open-World Learning Research

Publication

[1] Xiang Xiang①*, Zihan Zhang①, Xilin Chen. Curriculum-Balanced Long-Tailed Learning. Neurocomputing, 2024.

[2] Yuwen Tan, Qinhao Zhou, Xiang Xiang*, Ke Wang, Yuchuan Wu, Yongbin Li. Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer. In CVPR, 2024.

[3] Jing Ma, Xiang Xiang*, Ke Wang, Yuchuan Wu, Yongbin Li. Aligning Logits Generatively for Principled Black-Box Knowledge Distillation. In CVPR, 2024.

[4] Zihan Zhang①, Xiang Xiang①*. Decoupling MaxLogit for Out-of-Distribution Detection. In CVPR, 2023.

[5] Shouwen Wang, Qian Wan, Xiang Xiang*, Zhigang Zeng. Saliency Regularization for Self-Training with Partial Annotations. In ICCV, 2023.

[6] Xiang Xiang*, Yuwen Tan, Qian Wan, Jing Ma, Alan L. Yuille, Gregory D. Hager. Coarse-To-Fine Incremental Few-Shot Learning. In ECCV, 2022.

[7] Youming Deng, Yansheng Li, Yongjun Zhang, Xiang Xiang, Jian Wang, Jingdong Chen, Jiayi Ma. Hierarchical Memory Learning for Fine-Grained Scene Graph Generation. In ECCV, 2022. 

[8] Qinhao Zhou, Xiang Xiang*, Jing Ma. Hierarchical Task-Incremental Learning with Feature-Space Initialization Inspired by Neural Collapse. Neural Processing Letters, 2023.

[9] Yao Deng, Xiang Xiang*. Expanding Hyperspherical Space for Few-Shot Class-Incremental Learning. In WACV, 2024.

[10] Yuwen Tan, Xiang Xiang*. Cross-Domain Few-Shot Incremental Learning for Point-Cloud Recognition. In WACV, 2024.

[11] Yao Deng, Xiang Xiang*. Replaying Styles for Continual Semantic Segmentation Across Domains. In ACPR, 2023.

[12] Qian Wan, Shouwen Wang, Xiang Xiang. A Simple Unknown-Instance-Aware Framework for Open-Set Object Detection. In International Conference on Information Science and Technology, 2023.

[13] Xiao Wang, Xiang Xiang, Baochang Zhang, Xuhui Liu, Jianying Zheng, Qinglei Hu. Weakly Supervised Object Detection Based on Active Learning. Neural Processing Letters, 2022.

Medical Imaging Research

Publication

[1] Yuwen Tan, Xiang Xiang*, Yifeng Chen, Hongyi Jing, S Ye, C Xue, H Xu: Coupling Bracket Segmentation and Tooth Surface Reconstruction on 3D Dental Models. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023. MICCAI STAR Award

[2] Yuwen Tan, Xiang Xiang*: Boundary-Constrained Graph Network for Tooth Segmentation on 3D Dental Surfaces. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Machine Learning for Medical Imaging (MLMI), 2023.

[3] B. Li, Xiang Xiang, G. Huang, P. Wang, X. Han, D. Bai, H. Xu: A Coupled-Lines System to Determine the Anteroposterior Position of Maxillary Central Incisor for Smiling Profile Esthetics. The Angle Orthodontist, 2023.

[4] Y. Zhou, Yuwen Tan, Xiang Xiang, C. Xue, H. Xu. Research progress on deep learning algorithms to assist 3D tooth segmentation of digital dental models. Journal of Prevention and Treatment for Stomatological Diseases, 2023.

[5] Xiang Xiang, Z. Zhang, X. Peng, J. Shao: Learning-based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study. In International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI) workshop on Multiscale Multimodal Medical Imaging, 2022.

[6] Y. Tian, G. Huang, Xiang Xiang, N. Wang, W. Dai, J. Chen, D. Bai, H. Xu: The Lower Bow-Shaped Curve as a Novel Reference Frame to Determine the Lateral Limit of the Maxillary Anterior Arch for Smile Esthetics. American Journal of Orthodontics & Dentofacial Orthopedics, 2022. 

[7] Y. Wang, P. Wang, Xiang Xiang, H. Xu, Y. Tang, Y. Zhou, D. Bai, C. Xue: Effect of Occlusal Coverage Depths on the Precision of 3D-Printed Orthognathic Surgical Splints. BMC Oral Health, 2022.

Video Analysis Research

Dissertation

Xiang Xiang: Image-set, Temporal and Spatiotemporal Representations of Videos for Recognizing, Localizing and Quantifying Actions. Johns Hopkins University Ph.D. Dissertation, Baltimore, USA, June 2018. [pdf][talk-a][talk-b][official page at JHU Sheridan libraries][official announcement at JHU CS Dept][semanticscholar]

Xiang Xiang: Object Tracking and Segmentation in Dynamic Scenes: University of Chinese Academy of Sciences M.S. Dissertation, Beijing, China, June 2012. [PDF][bib]

Patent

H. Wang, Xiang Xiang, K. Zon. Subject Identification Systems & Methods. US 10832035B2, EP 3642757A1, CN 111095264A, WO 2018234542A1, JP 2020524850A.

Publication

[CVML]: for generic Computer Vision & Machine Learning; [CVHI]: for Computer Vision in Health Informatics; [MISC] for miscellaneous applications

[CVML] Xiang Xiang, F. Wang, Y. Tan, A. L. Yuille: Imbalanced Regression for Intensity Series of Pain Expression from Videos by Regularizing Spatio-Temporal Face Nets. Pattern Recognition Letters, 2022. 

[MISC] Xiang Xiang: Bootstrapping Autonomous Lane Changes with Self-Supervised Augmented Runs. In ECCV workshops, 2022.

[CVML] S. Li, Xiang Xiang: Lightweight Human Pose Estimation Using Heatmap-Weighting Loss. In ICPR workshops, 2022. Best Paper Award (Honorable Mention) 

[MISC] X. Wei, R. Qiu, H. Yu, Y. Yang, H. Tian, Xiang Xiang: Entropy-based Optimization via A* Algorithm for Parking Space Recommendation. International Conference on Traffic Engineering and Transportation System, Chongqing, China, 2021. [PDF]  

[CVML]: Xiang Xiang*, Z. Wang, S. Lao, B. Zhang*: Pruning Multi-view Stereo Net for Efficient 3D Reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 168, pp. 17-27, Elsevier, Oct. 2020 (*correspondence co-author). [Official][Preprint]

[CVML] J. Huang, Z. Huang, Xiang Xiang, X. Gong, B. Zhang: Long-Short Graph Memory Network for Skeleton-based Action Recognition. In IEEE Winter Conference on Applications of Computer Vision 2020 (WACV 2020), Aspen, USA. [PDF][Code][Slides]

[CVHI] Xiang Xiang: Beyond Deep Feature Averaging: Sampling Videos Towards Practical Facial Pain Recognition. Full paper at IEEE Conference on Computer Vision and Pattern Recognition 2019 (CVPR 2019) workshop on Face and Gesture Analysis for Health Informatics, Long Beach, USA. [PDF][thecvf][upmc.fr]

[CVML] Xiang Xiang and Trac D. Tran:  Linear Disentangled Representation Learning for Facial Actions. IEEE Transactions on Circuits and System  for Video Technology (IEEE T-CSVT), Volume: 28, Issue: 12, 2018. [ieee][arxiv][github][Modeling Work (new perspective): video-based sparsity + PCP]

[CVML] Xiang Xiang*, Ye Tian*, Austin Reiter, Gregory D. Hager, Trac D. Tran: S3D: Stacking Segmental P3D for Action Quality Assessment. Full paper IEEE International Conference on Image Processing 2018 (ICIP 2018), Athens, Greece. (* Equal contribution.)  [ieee][sigport][researchgate][github][linkedin][grant proposal][followup works at cvpr19]

[CVHI] Xiang Xiang: Effect of Spatial Alignment in Cataract Surgical Phase Recognition. Abstract paper at IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018) workshop on Fine-Grained Instructional Video Understanding, Salt Lake City, USA. [pdf][umich][poster]

[CVHI] Wentao Zhu, Xiang Xiang, Trac D. Tran, Gregory D. Hager and Xiaohui Xie: Adversarial Deep Structural Networks for Mammographic Mass Segmentation. Full paper at IEEE International Symposium on Biomedical Imaging 2018 (ISBI 2018), Washington DC, USA. [arxiv] [github][linkedin]

[CVHI] Xiang Xiang, Wentao Zhu, Trac D. Tran, Gregory D. Hager: Survey on Multi-Scale CNNs for Lung Nodule Detection. Abstract paper at IEEE International Symposium on Biomedical Imaging 2018 (ISBI 2018), Washington DC, USA.

[CVHC] Xiang Xiang*, Ye Tian*, Gregory D. Hager, Trac D. Tran: Assessing Pain Levels from Videos Using Temporal Convolutional Networks. Abstract paper at IEEE Winter Conf. on Applications of Computer Vision 2018 (WACV 2018) workshop on Computer Vision for Active and Assisted Living , Lake Tahoe, USA. (* Equal contribution.) [youtube]

[CVML] Feng Wang, Xiang Xiang, Jian Cheng, Alan L. Yuille. NormFace: L2 Hypersphere Embedding for Face Verification. Full long paper at ACM MultiMedia (MM) Conference 2017, Mountain View, USA. [arxiv][github][researchgate][comment][linkedin][csdn in Chinese][Theoretic work (3 new propositions): the norm of weights and features matter for nonlinear representation; although demonstrated on faces, this is a generic deep learning paper showing that when L2 norm is applied prior to softmax, the networks dont converge. The novel design is to add a scale layer that is learnt to help the network converge. Theoretical results are shown then lower bounds of loss with respect to the scale parameters] 

[CVHI] Feng Wang, Xiang Xiang*, Chang Liu, Trac D. Tran, Austin Reiter, Gregory D. Hager, Jian Cheng and Alan L. Yuille. Regularizing Face Verification Nets for Pain Intensity Regression. Full paper at IEEE International Conference on Image Processing 2017 (ICIP 2017), Beijing, China. (*corresponding author. Oral presentation.) [ieee][researchgate][arxiv] [github][slides][linkedin][Modeling work (new objective): fine-tuning a face net with a regression loss regularized by a classification loss to induce discrete values]

[CVML] Hao Zhu, Feng Wang, Xiang Xiang and Trac D. Tran. Supervised Hashing with Jointly Learning Embedding and Quantization. Full paper at IEEE International Conference on Image Processing 2017 (ICIP 2017), Beijing, China. [ieee][researchgate][linkedin][Modeling work (new objective): relaxed optimization for image retrieval] 

[CVML] Xiang Xiang, Dung N. Tran, Trac D. Tran. Sparse Unsupervised Clustering with Mixture Observations for Video Summarization.  Abstract paper at  IEEE Applied Imagery Pattern Recognition Workshop (AIPR) 2017, Washington DC, USA. [ieee][researchgate]

[CVML&HI] Xiang Xiang and Trac D. Tran:  Pose-Selective Max Pooling for Measuring Similarity. Workshop paper at IAPR International Conference on Pattern Recognition (ICPR) 2016 workshops, Cancun, Mexico. LNCS, vol. 10165 (Video Analytics), ISBN: 978-3-319-56686-3. [springer][github][researchgate][arxiv][aau][linkedin][google][Algorithmic work (2 new algorithms): video-based keyframe + deepface]

[CVML] Xiang Xiang and Trac D. Tran: Recursively Measured Action Units.  Workshop paper at IAPR International Conference on Pattern Recognition (ICPR) 2016 workshops, Cancun, Mexico. LNAI, vol. 10183 (Pattern Recognition of Social Signals), 978-3-319-59258-9. [researchgate][books.google][springer][Algorithmic work (new algorithm): video-based LSTM + SMP]

[CVML] Xiang Xiang, Minh Dao, Gregory D. Hager, Trac D. Tran:  Hierarchical Sparse and Collaborative Low-Rank Representation for Emotion Recognition. Full paper at IEEE International Conference on Acoustics, Speech, and Signal Processing 2015 (ICASSP 2015), Brisbane, Australia. ISBN: 978-1-4673-6997-8. [ieee][arxiv][github] [mathworks] [youtube] [elsevier][Modeling work (2 new objectives): video-based sparsity + PCP]

[CVHI] Xiang Xiang, Daniel Mirota, Austin Reiter, Gregory D. Hager:  Is Multi-Model Feature Matching Better for Endoscopic Motion Estimation? Workshop paper at International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2014 workshops, Boston, USA. LNCS, vol. 8899 (Comp.-Ass. & Rob. End.), 2014, ISBN: 978-3-319-13409-3. [springer][nih][researchgate][code][google][Empirical work: video-based 3D vision]

[CVML] Xiang Xiang, Hong Chang, Jiebo Luo: Online Web-Data-Driven Segmentation of Selected Moving Objects in Videos. Full paper at Asian Conference on Computer Vision 2012 (ACCV 2012):  134-146, Daejeon, Korean. LNCS, vol. 7725 (ACCV), 2013, ISBN: 978-3-642-37443-2. [demo: over 10 seconds!!!][springer][acm][pdf][researchgate][github][youtube] [dataset][google][cvpapers][visionbib][slides][Modeling work (new objective for segmentation + new algorithm for tracking): video-based MIL tracking + Graph cuts] 

[CVHI] Xiang Xiang: A Localization Framework under Non-rigid Deformation for Rob. Surg.. Full paper at International Symposium on Visual Computing (ISVC) 2011:  11-22, Lake Tahoe, USA. LNCS, vol. 6938 (Advances in Visual Computing), 2011, ISBN: 978-3-642-24027-0. [springer][acm][linkedin][researchgate][google][visionbib][Modeling work (new formulation): geometry based registration]

[CVML] Xiang Xiang:  An Attempt to Segment Foreground in Dynamic Scenes. Full paper at International Symposium on Visual Computing (ISVC) 2011: 124-134, Lake Tahoe, USA. LNCS, vol. 6938 (Advances in Visual Computing), 2011, ISBN: 978-3-642-24027-0. [springer][acm][researchgate][google][Empirical work: video-based graph cuts] 

[CVML] Xiang Xiang, Wenhui Chen, Du Zeng: Intelligent Target Tracking and Shooting System with Mean Shift. Full paper at IEEE International Symposium on Parallel and Distributed Processing and Applications (ISPA) 2008:  417-421, Sydney, Australia. Parallel and Distributed Processing, IEEE, 2008, ISBN: 978-0-7695-3471-8. [ieee][pdf][github][youtube][whu][sjtu][Embedded vision work: video-based tracking camera] [Interesting cited paper] (a typical paper from this conference; no free lunch, but welcome to the jungle! It turns out that I start doing multi-core 10 years ago in college and observe it move to multiple CPU/GPU which induces great success of modern vision and graphics applications.) 

Research interest

I work in the area of video, facial & medical image analysis and health informatics. I specialize in action quality assessment, tracking and temporal modeling, and restrictive representation learning. See also my research proposal.


Patent

[CVHC] Haibo Wang, Xiang Xiang, Kees van Zon. Subject identification systems and methods. US Patent, No. 16/014,046, 2018. [google] [System work: proposed a hybrid face recognition solution for patient identification]


Write-ups

Fresh Research, Colorful Life, 2009. [google]

A Brief Review on Visual Tracking Methods, 2011. [google][researchgate]

Xiang Xiang and Daniel Gildea: EM Algorithm Lecture Notes, 2012.  [rochester]

Surgical Phase Recognition, 2012. [dropbox][youtube] (eye tracking)

One Way to Implement Trimmed ICP algorithm, 2012. [dropbox]

Learning CRFs for Stereo Matching, 2013. [dropbox]

A Review on Stereo Matching Using Belief Propagation, 2013. [dropbox]

Xiang Xiang, Fabian Prada and Hao Jiang. Joint Sparse and Low-Rank Representation for Emotion Recognition, 2014. [dropbox]

Sample GBO Questions on Computer Vision, Machine Learning and Optimization, 2014. [blogger]

Tutorial on setting up Caffe, 2014. [dropbox]

From Receipts to Charts: Automating the Accounting Workflow. MASC-SLL, JHU, 2015 [researchgate]. 

A Review on EigenFace, 2015. [dropbox][youku]

A Review on Dimensionality Reduction, 2015. [dropbox]

A Survey on Audio-Visual Speech Recognition, 2015. [researchgate][linkedin]

A C++ Solution for Testing the VGG_Face Deep Model, 2016. [github][linkedin]

Soccer-Field Computer Vision, 2016. [linkedin]

A Future of Fully-Auto Production of Animations, 2016. [linkedin]

Extraction of Object Skeleton, 2016. [linkedin][dropbox]

Multi-Scale Deep 3D CNNs for Automatic Detection and Segmentation of Pulmonary Nodules, 2017. [dropbox]

A Brief Review on Video Representation, 2018. [slideshare]

Biography

Dr. Xiang Xiang is currently an associate professor of computer science and technology at Huazhong University of Science and Technology (HUST), Wuhan, China, where he founds and directs the HUST AIA Image and Vision Learning Lab (HAIV Lab) since 2021. He is a senior member of China Society of Image and Graphics. He has heavily published papers at venues such as CVPR, ICCV, ECCV, ACM Multimedia, MICCAI, ISBI, ICIP, ICASSP, WACV, etc., and been honorably mentioned for the AI 2000 Most Influential Scholar Award, in recognition of outstanding and vibrant contributions in the field of multimedia between 2013 and 2022. His advisees at HAIV Lab has won awards such as Best Paper Award at ICPR 2022 T-CAP workshop, MICCAI 2023 STAR Award, and so on. He has been an applied scientist at AWS AI Labs, Seattle, USA, since 2018, until moving to TuSimple, San Diego, USA, as a senior research scientist in 2020. Before that, he received the Ph.D. degree at Johns Hopkins University (JHU), Baltimore, USA, in Summer 2018, the M.S.E. degree from JHU in Summer 2014, the M.S. degree from the Institute of Computing Technology at Chinese Academy of Sciences and namely the University of Chinese Academy of Sciences, Beijing, China, in Summer 2012, the B.S. degree from Wuhan University, Wuhan, China, in Summer 2009, all in Computer Science. 

He studies computer vision and machine learning, with an interest in open-world robust learning and particularly a focus on incremental learning. For a glimpse of his research, please refer to his Google Scholar page & DBLP page and follow links to papers therein. His academic genealogy can be found here and his Erdös Number is 5. For more detailed biography, please see also http://www.cs.jhu.edu/~xxiang/ or drop a line at xxiang@cs.jhu.edu .

Service

Associate Editor for AMIS Journal: Mathematical Foundations of Computing; Reviewer for Pattern Recognition'18-, Proceedings of IEEE, IEEE series Trans. Pattern Analysis and Machine Intelligence'19-, Trans. Neural Networks and Learning Systems'18-, Trans. Image Processing'18-, Trans. Circuits and Systems for Video Technology'15-, Trans. Affective Computing'18-, Trans. Biometrics, Behavior, and Identity Science'19-, as well as IEEE Access, IEEE Geoscience and Remote Sensing Letters, Multimedia Tools and Applications'18-, SPIE Journal of Electronic Imaging'18-, CVPR'19, ACM MM'19, IROS'19, ICCV’15, MICCAI17’16’15, ICIP’18'17'12'11, ICASSP’17, ICRA’14, and ICME’12. 

Technical staff of CVPR'17 PASCAL in detail challenge.  Program committee member of ACM MM'19, DLMIA@MICCAI'17 and AMFG@CVPR'18.

Liability

If you disagree with any content in this webpage, the first thing you need to do is to drop a line to xxiang@cs.jhu.edu . Any comment is welcome and will be accommodated with the best sincerity. Thanks for your cooperation!