Qixiang Ye is a full professor with the University of Chinese Academy of Sciences since 2016. He the funding directors of learning and machine perception (LAMP) lab. He received the B.S. and M.S. degrees in mechanical and electrical engineering from Harbin Institute of Technology, China, respectively, and the Ph.D. degree from the Institute of Computing Technology, Chinese Academy of Sciences. He was a visiting assistant professor with the Institute of Advanced Computer Studies (UMIACS), University of Maryland, College Park until 2013 and a visiting scholar of Duke University EECS in 2016. His research interests include representation learning, imaging and image sensing. He has more than 100 papers published in refereed conferences and journals including IEEE TPAMI, TIP, TNNLS, TITS, CVPR, ICCV, ECCV, AAAI, and NeurIPS. He is an AC of CVPR2023, ICLR2024, NeurIPS2024, NeurIPS2025 and on the editor board of IEEE Transactions on Intelligent Transportation System and IEEE Transactions on Circuit and System on Video Technology.

Sep. 2024, Four papers accepted by NeurIPS and AAAI Con![Yue,Mingxiang,Jihao, yunjie]

Jul. 2024, Xiaosong is honored  Excellent Doctoral Dissertation Award. Con! [Xiaosong]

Jul. 2024, iTPN is accepted by TPAMI Con![yunjie]

Jun. 2023, 3 paper is accepted by AAAI2024、CVPR204、ECCV2024[Hongtian, Mingyue, Yuzhong, Liufeng].

Jul. 2023, 2 papers are accepted by ICCV 2023. Con![Yuzhong, Liufeng].

Mar. 2023, 4 papers are accepted by CVPR 2023. Con![yunjie, huangwei, mingxiang, zhaozhi].

Jan. 2023，HiViT and Conformer are accepted by ICLR 2023 and TPAMI Con! [Xiaosong, Zhiliang].

Mar. 2022, Dynamic Support Network paper accepted by TPAMI. Con! [Bob]

July. 2021, Three Papers Accepted by ICCV2021, Con! [Yi, Zhiliang, Wei]!

Mar. 2021, Five Papers Accepted by CVPR 2021, Con![Binghao, Tianning, Zonghao, Bohao, Yuchao]

Feb. 2021, Harmonic Feature Activation (HFA) for few-shot learning is accepted by TIP. Con! [Binghao]

Jan. 2021, FreeAnchor is accepted by T-PAMI. Con! [Xiaosong]

Nov. 2020, A ToolBox for general image-to-mask tasks SDL-Skeleton is released. [Code]

Oct. 2020, Fang Wan Recieved 100 Outstanding Ph.D thesis, CAS, [Con! Fang]

Aug. 2020, Anual Progress Review at Valse 2020 [slides]

Jul. 2020: Two papers are accepted by ECCV 2020. Con![Boyu, Pengxu] 

Mar. 2020: Five papers are accepted by CVPR 2020. Con![WeiKe,Chang,Yi, YaoYuan, Yunpeng] 

1. Bachelor and Master of science, Harbin Institute of Technology, China
2. Ph.D in computer science, Institute of Computing Technology, Chinese Academy of Sciences
3. Visiting Assitant Professor of University of Maryland, College Park
4. Visiting Scholar of Electrical and Computer Engineering, Duke 
5. Full Professor of University of Chinese Academy of Sciences

0. Yufan Liu              PostDoc Since 2023. Research Areas: AI4X 

1. Mingxiang Liao    Ph.D student since fall 2020. Research Areas:  Interative Vision Model

2. Zhaozhi Wang       Ph.D  student since fall 2023. Research Areas:  Representation Learning

3. Hongtian Yu         Ph.D student since fall 2021. Research Areas:  AI4Science

4. MingyueGuo        Ph.D student since fall 2021. Research Areas:  Visual Object Detection

5. Zhaozhi Wang      Ph.D student since fall 2022. Research Areas:  Visual Representation Learning

7. Yue Liu                 Ph.D student since fall 2020. Research Areas:  Multi-Modality Model

8. Tianren Ma           Ph.D student since fall 2022. Research Areas:  Multi-Modality Generative  Model

9. Wenxi Yang          Ph.D student since fall 2024. Research Areas:  AI4Science

10. Jingwei Wu        Master student since fall 2022. Research Areas:  Visual Representation Learning

11. Ang Li                Master student since fall 2022. Research Areas:  Multi-Modality Model

12. Conghu Li          Master student since fall 2022. Research Areas:  Vision Model

13. Jihao Qiu            Master student since fall 2023. Research Areas:  Multi-Modality Model

14. Mu Zhang          Master student since fall 2023. Research Areas:  Generative Model

15. Wei Yang            Master student since fall 2023. Research Areas:  Visual Object Detection

1. Bo Wu                (2011)  Master , Visual Object Detection, Agricalture Bank of China
2. Dongsheng Yu   (2011)   Master, , Visual Object Detection,  Tecent
3. Jixiang Liang      (2013)  Master, Visual Object Detection, Agricalture Bank of China  (Hornored Natinal Scholarship 2012) 
4. Yaozhang           (2015)  Master, Visual Object Detection,   China Aerospace Science and Industrial Corporation
5. Yanting Cui         (2015)  Master, Visual Object Detection,  Alibaba
6. Xiansong Huang (2017)  Master, Visual Object Detection, Ctrip
7. Yanran  Liu         (2017)  Master, Visual Object Detection,  Datang Telecom
8. Wei Ke                (2018)  Ph. D , Object and Symmetry Detection, Carnegie Mellon University，  (Hornored Natinal Scholarship, CAS President Scholarship 2017) 

9. Caijing Miao         (2019) Master, Weakly Supervised Object Detection, KingSoft

10. Fang Wan          (2019)  Ph. D, Weakly Supervised Object Detection, UCAS

11. Haolan Xue        (2020) Master, Weakly Supervised Object Detection, Alibaba DAMO

12. Tianliang Zhang (2020) Ph. D,  Visual Object Detection, Tecent,  (Hornored Natinal Scholarship 2020) 

13. Chang Liu           (2021)  Ph. D,  Self-supervised Learning, Tsinghua University, (Hornored Natinal Scholarship 2020) 

14. Wei Gao              (2021) Master, Weakly Supervised Object Detection, Alibaba

15.  Tianing Yuan      （2022）Master, Active Object Detection, Huawei

16. Yunxiao Zhang    （2022）Master,  Few-shot Incremental Learning

17. Zixiang Zhong    （2022）Master,  Few-shot Incremental Learning

18. Jinge Ma             (2021) Bachelor, Visual Object Detection,  Purdue University, USA     

19. Yumeng Zhang  (2022) Bachelor,  Johns Hopkins University, USA

20.  Yuan Yao          (2023)  Ph.D, Huawei

21.  Xiaosong Zhang   (2023)   Ph.D, Beijing Zhiyuan Research

22.  Boyu Yang  (2023)  Ph. D, China Mobile

23. Bohao Li   (2023)  Ph. D student, CUHK

24.  Binghao Liu    (2023)      Ph. D, Ali baba

25. Letian Shen (2024)         Bachelor, Brown University

26.  ZongHao  Guo (2024)     Ph. D, QiYuan Lab.

27.  Yunjie Tian (2024)          Ph. D, University at Buffalo

28.  Mengying Fu  (2024)      Ph.D., Beijing Municipal Government 

29.  Zhiliang Peng  (2024)     Ph. D, MSRA

1.Outstanding Researcher of Youth Innovation Promotion Association, Chinese Academy of Sciences, 2018. 

2."LuJiaXi" Outstanding Young Researcher Awards, 2014
3.Top-10 most Cited Papers in The Past Five Years of Image and Vision Computing, 2010
4.Outstanding Course of Chinese Academy of Sciences, 2007 and 2009
5.CAS-Sony Outstanding Paper Award 2005
6.Scholarship of Institute of Computing Technology, Chinese Academy of Sciences, 2005

Representation Learning

[1] Z. Peng, Z. Guo, W. Huang, Y. Wang, L. Xie,  J. Jiao, Q. Tian, Q. Ye, "Conformer: Local Features Coupling Global Representations for Recognition and Detection," IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), 45(8): 9454-9468 (2023). [pdf][code] 

[2] X. Zhang, Y. Tian, W. Huang, Q.  Ye, Q. Dai, L.  Xie, Q. Tian, "HiViT: A Simpler and More Efficient Design of Hierarchical Vision Transformer", ICLR 2023. [pdf][source_code].

[3] Y. Tian, L. Xie, Z. Wang, L. Wei, X. Zhang, J. Jiao, Y. Wang, Q. Tian, Q. Ye, Integrally Pre-Trained Transformer Pyramid Networks, CVPR 2023, TPAMI2024 [pdf][code]

[4] W. Huang, Z. Peng, L. Dong, F. Wei, J. Jiao, Q. Ye, Generic-to-Specific Distillation of Masked Autoencoders,  CVPR 2023 [pdf][code]

[5] C. Liu, Y. Yao, D. Luo, Y. Zhou, Q. Ye, "Self-supervised Motion Perception for Spatio-temporal Representation Learning,"  IEEE Trans. Neural Networks Learn. Syst. (TNNLS), 2022. [pdf][source-code].

[6] Y. Yao, C. Liu, D. Luo, Y. Zhou, Q. Ye, "Video Playback Rate Perception for Self-Supervised Spatio-Temporal RepresenationLearning," IEEE CVPR, 2020. [pdf][source-code]. 

[7] Z. Peng, W. Huang, S. Gu, L. Xie, Y. Wang, J. Jiao, Q. Ye, Conformer: Local Features Coupling Global Representations for Visual Recognition, ICCV 2021. [pdf][code]

[8] Y. Zhou, Q. Ye, Q. Qiu, and J. Jiao, "Oriented Response Networks," IEEE CVPR, 2017. [pdf]-[source code]-[bibtex]

Object Sensing

[9] M. Guo, Z. Yan, Q. Ye,  "Regressor-Segmenter Mutual Prompt Learning for Crowd Counting,"IEEE CVPR 2024. [pdf].

[12] B. Yang, M. Lin, Z. Zhang, B. Liu, X. Liang, R. Ji, Q. Ye, "Dynamic Support Network for Few-shot Incremental Learning," IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), 45(3): 2945-2951 (2023). [pdf][source-code].

[13] F. Wan, Q. Ye, S. Xu, J. Liu, X. Ji, Q. Huang, "Multiple Instance Differentiation Learning for Active Object Detection," IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), 45(10): 12133-12147 (2023). [pdf][source_code]

[14] B. Liu, B. Yang, X. Chen, R. Wang, Q.Ye, "Learnable Distribution Calibration for Few-Shot Class-Incremental Learning," IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), 45(10): 12699-12706 (2023). [pdf][source_code]

[16] T. Yuan, F. Wan, M. Fu, J. Liu, C. Xu, X. Ji, Q. Ye, "Multiple Instance Active Learning for Object Detection," IEEE CVPR 2021[pdf][source_code].

[17] Z. Guo, C. Liu, X. Zhang, J. Jiao, X. Ji, Q. Ye, "Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection," IEEE CVPR 2021 [pdf][source_code].

[18] B. Li, B. Yang, C. Liu, F. Liu, R. Ji, Q. Ye, "Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection," IEEE CVPR 2021 [pdf][source_code].

[19] B. Liu, Y. Ding, J. Jiao, X. Ji, Q. Ye, "Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation," IEEE CVPR 2021[pdf][source_code].

[20] W. Ke, T. Zhang, Z. Huang, Dong Huang, Q. Ye, "Multiple Anchor Learning for  Visual Object Detection, " IEEE CVPR, 2020[pdf][source_code]. 

[21] B. Yang, C Liu, B. Li, J. Jiao,  Q. Ye, "Prototype Mixture Models for Few-shot Semantic Segmentation," ECCV 2020. [pdf][source-code].

[22] X. Zhang, F. Wan, C. Liu, R. Ji, Q. Ye, "FreeAnchor: Learning to Match Anchors for Visual Object Detection," NeurIPS 2019. [pdf][source-code]

[23] W. Gao, F. Wan, etc., B. Zhou, Q. Ye，TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization, ICCV 2021. [pdf][code]

[24] Q. Ye, F. Wan, C. Liu, Q. Huang, X. Ji,  "Continuation Multiple Instance Learning for Weakly and Fully Supervised Object Detection, "  IEEE Trans. Neural Networks Learn. Syst. (TNNLS),  2022:30(10):5452-5466  . [pdf][source_code]

[25] B. Yang, F. Wan, B. Li, C. Liu, X. Ji, Q. Ye, "Part-based Semantic Transform for Few-shot Semantic Segmentation,"  IEEE Trans. Neural Networks Learn. Syst. (TNNLS) ,  33(12):7141 - 7152, 2022. [pdf]

[26] H. Xue, C. Liu, F. Wan, J. Jiao, Q. Ye, "DANet: Divergent Activation for Weakly Supervised Object Localization," IEEE ICCV 2019. [pdf] [source-code]

[27] F. Wan, C. Liu, X. Ji, J. Jiao, Q. Ye, "CMIL: Continuation Multiple Instance Learning for Weakly Supervised object Detection," IEEE CVPR, 2019 (Oral). [pdf][source-code][Caffe+ResNet]

[28] Y. Zhu, Y. Zhou, H. Xu, Q. Ye, D. Doeramann, J. Jiao, "Learning Instance Activation Maps for Weakly Supervised Instance Segmentation," IEEE CVPR, 2019 [pdf].

[29] C. Miao, X. Xie, F. Wang, C. Su, J. Jiao, Q. Ye, "SIXray: A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images," IEEE CVPR, 2019 [pdf][dataset&code]

[30] F. Wan, P. Wei, Z. Han, J. Jiao, Q. Ye, “Min-entropy Latent Model for Weakly Supervised object Detection,” IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), 41(10): 2395-2409 (2019). [IEEE PAMI PDF][Arxiv PDF][Code][pytorch-code]

[31] F. Wan, P. Wei, Z. Han, J. Jiao, Q. Ye, “Min-entropy Latent Model for Weakly Supervised object Detection,” IEEE CVPR, 2018: 1297-1306. [pdf]-[source code]-[bibtex]

[32] Y. Zhou, Y. Zhu, Q. Ye, Q. Qiu, J. Jiao, “Weakly Supervised Instance Segmentation using Class Peak Response,” IEEE CVPR, 2018 (Spotlight). [pdf]-[source code]-[bibtex]

[33] W. Deng, L. Zheng, Q. Ye, J. Jiao, “Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification,” IEEE CVPR, 2018.  [pdf]-[source code]-[bibtex]

[34] Q. Ye, T. Zhang, Q. Qiu, B. Zhang, J. Chen, and G. Sapiro, "Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model," IEEE CVPR, 2017. [pdf]-[source code]-[bibtex]

[35] Y. Zhu, Y. Zhou, Q. Ye, Q. Qiu, and J. Jiao, "Soft Proposal Network for Weakly Supervised Object Localization," IEEE ICCV, 2017. [pdf]-[source code]-[bibtex]

[36] Y. Ding, Y. Zhou, Y. Zhu, Q. Ye, J. Jiao, "Selective Sparse Sampling for Fine-grained Image Recognition,"  IEEE ICCV, 2019. [pdf][source-code]

[37] C. Liu, W. Ke, F. Qin, Q. Ye, "Linear Span Network for Object Skeleton Detection," ECCV, 2018. [pdf]-[source code]-[bibtex]

[38] W. Ke, J. Chen, J. Jiao, and Q. Ye, "SRN: Side-output Residual Network for Object Symmetry Detection in the Wild," IEEE CVPR. (Oral). [pdf]-[source code]-[bibtex]

[39] Q. Ye, D. S. Doermann, "Text Detection and Recognition in Imagery: A Survey,"  IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), 37(3):1480-1500 (2015). [pdf]-[bibtex]

[40] Q. Ye, Z. Han, J. Jiao, J. Liu, "Human Detection in Images via Piecewise Linear Support Vector Machines," IEEE Trans. Image Process. (TIP), 22(2):778-789 (2013). [pdf]- [source code]-[bibtex] 

Machine Learning from 2014-2020 Slides
Reinforecment Learning: from AlhpaGo to AlphaZero 2018 Spring, 2019 Spring slides