Li Liu

Li Liu

PhD

Researcher

Biography

Li Liu received the B.Sc. degree in communication engineering, the M.Sc. degree in photogrammetry and remote sensing and the Ph.D. degree in information and communication engineering from the National University of Defense Technology, China, in 2003, 2005 and 2012, respectively. During her PhD study, she spent more than two years as a Visiting Student at the University of Waterloo, Canada, from 2008 to 2010. From 2015 to 2016, she visited the Multimedia Laboratory at the Chinese University of Hong Kong. Li Liu is currently an Assistant Professor with the Center for Machine Vision and Signal Analysis, University of Oulu, Finland, where she has been a senior researcher since 2016.

Personal Homepage: http://www.ee.oulu.fi/~lili/LiLiuHomepage.html

Research interests

  • Computer Vision
  • Machine Learning
  • Pattern Recognition
  • Image Understanding

Recent Research Topics

Research on energy, data and label efficient AI aims to create approaches to understand the content of images and videos with ambitious goals:

  • being computing and energy efficient without sacrificing prediction accuracy or increasing hardware cost, especially suitable for on-device AI;
  • reducing the amount of labeled samples in AI systems and the amount of data necessary to adapt models to new environments;
  • using as little labeled training data as people need, and to generalize to new tasks beyond the one task the model was trained on.

Our research is expected to significantly benefit AI powered edge devices to support a wide range of computer vision tasks in various applications, and to overcome the current limitations of Deep Neural Networks and to narrow the gap between AI and real human intelligence.

Selected publications

 

   Published Journal Articles:

  1. X. Liu, M. Li, C. Tang, J. Xiong, J. Xia, Li Liu, M. Kloft, and E. Zhu, Efficient and Effective Regularized Incomplete Multiview Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
  2. Y. Guo, H. Wang, Q. Hu, H. Liu, Li Liu, and M. Bennamoun, Deep Learning for 3D Point Clouds: A Survey,IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
  3. Y. Guo, Li Liu, Z. Lin, X. Deng, W. An and L. Wang, Deep Video Superresolution using HR Optical Flow Estimation, IEEE Transactions on Image Processing, 2020.
  4. Li Liu, J. Chen, P. Fieguth, G. Zhao, M. Pietikäinen, R. Chellappa, From BoW to CNN: Two Decades of Texture Representation for Texture Classification, International Journal of Computer Vision, vol. 127, pp. 74-109, 2019. [pdf]
  5. Li Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu, M. Pietikäinen, Deep Learning for Generic Object Detection: A Survey, International Journal of Computer Vision, 2019.[pdf]
  6. Li Liu, M. Pietikainen, J. Chen, G. Zhao, X. Wang, R. Chellappa. Guest Editors’ Introduction to the Special Section on Compact and Efficient Feature Representation and Learning in Computer Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(10): 2287-2290.
  7. Y. Liu, W. Chen, Li Liu, M S. Lew. SwapGAN: A Multistage Generative Approach for Person-to-Person Fashion Style Transfer. IEEE Transactions on Multimedia, 2019.
  8. Li Liu, J. Chen, G. Zhao, P. Fieguth, X. Chen, M. Pietikäinen, Texture Classification in Extreme Scale Variations using GANet, IEEE Transactions on Image Processing, vol. 28, pp. 3910-3922, 2019. 
  9. X. Liu, L. Wang, X. Zhu, M. Li, E. Zhu, T. Liu, Li Liu, Y. Dou, J. Yin, Absent Multiple Kernel Learning Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
  10. S. Zhou, X. Liu, M. Li, E. Zhu, Li Liu, C. Zhang, J. Yin, Multiple Kernel Clustering With Neighbor Kernel Subspace Segmentation, IEEE Transactions on Neural Networks and Learning Systems, 2019.
  11. J. Wu, W. Zhuge, X. Liu, Li Liu, C. Hou, Fragmentary Multiinstance Classification, IEEE Transactions on Cybernetics, 2019.
  12. X. Zhao, Y. Lin, Li Liu, J. Heikkilä, W. Zheng, Dynamic Texture Classification Using Unsupervised 3D Filter Learning and Local Binary Encoding. IEEE Transactions on Multimedia, vol. 21, pp. 1694-1708, 2019. 
  13. Q. Ling, Y. Guo, Z. Lin, Li Liu, W. An, A Constrained Sparse Representation Based Binary Hypothesis Model for Target Detection in Hyperspectral Imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, pp. 1933-1947, 2019. 
  14. Y. Liu, Li Liu, Y. Guo, M. Lew, MMCNet: A Unified Network for Multimodal Matching and Classification, Pattern Recognition, vol, 84, pp. 51-67, 2018.
  15. Li Liu, P. Fieguth, Y. Guo, X. Wang, M. Pietikäinen, Local Binary Features for Texture Classification: Taxonomy and Experimental Study, Pattern Recognition, vol. 62, pp. 135-160, 2017.
  16. J. Chen, V. Patel, Li Liu, V. Kellokumpu, G. Zhao, M. Pietikäinen, R. Chellappa, Robust Local Feature for Remote Face Recognition, Image and Vision Computing, vol. 64, pp. 34-46, 2017.
  17. Li Liu, S. Lao, P. Fieguth, Y. Guo, X. Wang, M. Pietikäinen, Median Robust Extended Local Binary Pattern for Texture Classification, IEEE Transactions on Image Processing, vol. 25, no. 3, pp. 1368-1381, 2016. 
  18. Li Liu, P. Fieguth, G. Zhao, M. Pietikäinen, D. Hu, Extended Local Binary Patterns for Face Recognition, Information Sciences, vol. 358-359, no. 1, pp. 56-72, 2016. 
  19. Li Liu, L. Wang, L. Zhao, P. Fieguth, Random Projections and Single BoW for Fast and Robust Texture Segmentation, Information Sciences, vol. 370-371, no. 20, pp. 428-445, 2016.
  20. Y. Guo, Y. Lei, Li Liu, Y. Wang, M. Bennamoune, Ferdous Sohel, EI3D: Expression Invariant 3D Face Recognition based on Feature and Shape Matching, Pattern Recognition Letters, vol. 83, pp. 403-412, 2016. 
  21. Li Liu, Y. Wei, P. Fieguth, G. Kuang, Fusing Sorted Random Projections for Robust Texture Classification and Material Categorization, IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 3, pp. 482-496, 2015.
  22. Li Liu, Y. Long, P. Fieguth, S. Lao, G. Zhao, BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification, IEEE Transactions on Image Processing, vol. 23, no. 7, pp. 3071-3084, 2014. 
  23. L. Liu and P. Fieguth, Texture Classification from Random Features, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 574-586, 2012.
  24. Li Liu, P. Fieguth, D. Clausi, G. Kuang, Sorted Random Projections for Robust Rotation Invariant Texture Classification, Pattern Recognition, vol. 45, no. 6, pp. 2405-2418, 2012.
  25. N. Wang, G. Shi, Li Liu, L. Zhao, G. Kuang, Polarimetric SAR Target Detection Using the Reection Symmetry, IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 6, pp. 1104-1108, 2012.
  26. Li Liu, L. Zhao, Y. Long, G. Kuang, P. Fieguth, Extended Local Binary Patterns for Texture Classification, Image and Vision Computing, vol. 30, no. 2, pp. 80-99, 2012. 
  27. G. Gao, Li Liu, L. Zhao, G. Shi, G. Kuang, An Adaptive and Fast CFAR Algorithm based on Automatic Censoring for Target Detection in High-Resolution SAR Images, IEEE Transactions on Geoscience Remote Sensing, vol. 47, no. 6, pp. 1685-1697, 2009.

  Conference Proceedings:

  1. Z. Su, M. Pietikäinen, Li Liu BIRD: Learning Binary and Illumination Robust Descriptor for Face Recognition, The British Machine Vision Conference (BMVC), 2019.
  2. J. Sui, Z. Liu, Li Liu., A. Jung, T. Liu, B. Peng, X. Li, Sparse Subspace Clustering for Evolving Data Streams, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7455-7459, 2019.
  3. X. Zhao, Y. Lin, Li Liu, Dynamic Texture Recognition Using 3D Random Features, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2102-2106, 2019.
  4. X. Zhu, X. Liu, M. Li, E. Zhu, Li Liu, Z. Cai, J. Yin, W. Gao, Localized Incomplete Multiple Kernel kmeans, International Joint Conference on Artificial Intelligence (IJCAI), 2018. 
  5. N. Liu, B. Zhang, Y. Zong, Li Liu, J. Chen, G. Zhao, J. Zhu, Super Wide Regression Network for Unsupervised Cross Database Facial Expression Recognition, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  6. N. Liu, Y. Zong, B. Zhang, Li Liu, J. Chen, G. Zhao, J. Zhu, Unsupervised Cross Corpus Speech Emotion Recognition Using Domain Adaptive Subspace Learning, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),2018.
  7. Y. Guo, Y. Liu, M. Boer, Li Liu, M. Lew, A Dual Prediction Network For Image Captioning, IEEE International Conference on Multimedia and Expo (ICME), 2018.
  8. Xin Zhang, Li Liu, Yuxiang Xie, J. Chen, Lingda Wu and M. Pietikäinen, Rotation Invariant Local Binary Convolution Neural Networks, International Conference on Computer Vision Workshop on Compact and Efficient Feature Representation and Learning in Computer Vision, 2017.
  9. S. Guo, Li Liu, W. Wang, S. Lao, Liang Wang, An Attention Model Based on Spatial Transformers for Scene Recognition, International Conference on Pattern Recognition, 2016. 
  10. Li Liu, P. Fieguth, X. Wang, M. Pietikäinen, Evaluation of LBP and Deep Texture Descriptors with A New Robustness Benchmark, European Conference on Computer Vision (ECCV), 2016. 
  11. Li Liu, P. Fieguth, M. Pietikäinen and S. Lao, Median Robust Extended Local Binary Pattern for Texture Classification, IEEE International Conference on Image Processing (ICIP), 2015, Oral Presentation. 
  12. Li Liu, P. Fieguth, G. Zhao and M. Pietikäinen, Extended Local Binary Pattern Fusion for Face Recognition, IEEE International Conference on Image Processing (ICIP), 2014, Oral Presentation. 
  13. Li Liu, B. Yang, P. Fieguth, Z. Yang and Y. Wei, BRINT: A Binary Rotation Invariant And Noise Tolerant Texture Descriptor, IEEE International Conference on Image Processing (ICIP), 2013.
  14. Li Liu, P. Fieguth, G. Kuang, H. Zha, Sorted Random Projections for Robust Texture Classification, International Conference on Computer Vision (ICCV), 2011.
  15. Li Liu, P. Fieguth and G. Kuang, Combining Sorted Random Features for Texture Classification, International Conference on Image Processing (ICIP), 2011. 
  16.  Li Liu, P. Fieguth and G. Kuang, Generalized Local Binary Patterns for Texture Classification, British Machine Vision Conference (BMVC), 2011.
  17. Li Liu, P. Fieguth and G. Kuang, Compressed Sensing for Robust Texture Classification, Asian Conference on Computer Vision (ACCV), 2010, Oral Presentation.
  18. Li Liu and P. Fieguth, Texture Classification Using Compressed Sensing, Canadian Conference on Computer and Robot Vision (CRV), 2010.

  Special Issue of A Journal:

  1. Li Liu, M Pietikainen, J Qin, W Ouyang, LV Gool, Guest Editorial:  Efficient Visual Recognition, International Journal of Computer Vision, 2020. (IF:5.698, JUFO Ranking:3)
  2. Li Liu, M. Pietikäinen, J. Chen, G. Zhao, X. Wang, R. Chellappa, Guest Editors’ Introduction to the Special Section on Compact and Efficient Feature Representation and Learning in Computer Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 10, 2019.
  3. Leading Guest Editor (Li Liu, M. Pietikäinen, J. Chen, J. Qin, W. Ouyang and L. Gool),  International Journal of Computer Vision Special Issue on Efficient Visual Recognition (http://www.ee.oulu.fi/~lili/IJCVSIEVR2018.htm). 
  4. CoEditor (with J. Chen, Z. Lei, Li Liu, G. Zhao and M. Pietikäinen), Special Issue on Robust Local Descriptors for Computer Vision, Neurocomputing, vol. 184, 2016.

Databases

Professional and community activities

  • Area Chair, IEEE International Conference on Multimedia and Expo (ICME), 2020.
  • Associate Editor, The Visual Computer, 2017–present.
  • Associate Editor, Pattern Recognition Letters, since 2020
  • Guest Editor, International Journal of Computer Vision (Special Issue on Efficient Visual Recognition), 2018-2020.
  • Guest Editor, IEEE Transactions on Pattern Analysis and Machine Intelligence (Special Issue on Compact and Efficient Feature Representation and Learning in Computer Vision), 2018-2019.
  • Guest Editor, Neurocomputing (Special Issue on RoLoD: Robust local descriptors for computer vision).
  • Chair, Tutorial on Textures, Objects, Scenes: From Handcrafted Features to CNNs and Beyond, CVPR, Long Beach, United States, 2019.
  • Main organizer, Joint Workshop on Efficient Deep Learning in Computer Vision, CVPR, Washington, United States, 2020.
  • Cochair, Joint Workshop on Multimodal Learning, CVPR, Washington, United States, 2020.
  • Main organizer, 4th CEFRL International Workshop on Compact and Efficient Feature Representation and Learning in Computer Vision, ICCV, Seoul, Korea, 2019.
  • Cochair, 1st CroMoL International Workshop on Cross Modal Learning in Real World, ICCV, Seoul, Korea, 2019.
  • Main organizer, 3rd CEFRL International Workshop on Compact and Efficient Feature Representation and Learning in Computer Vision, CVPR, Long Beach, United States, 2019.
  • Main organizer, 2nd CEFRL International Workshop on Compact and Efficient Feature Representation and Learning in Computer Vision, ECCV, Munich, Germany, 2018.
  • Main organizer, 1st CEFRL International Workshop on Compact and Efficient Feature Representation and Learning in Computer Vision, ICCV, Venice, Italy, 2017.
  • Cochair, RoF International Workshop on Robust Features for Computer Vision, CVPR, Las Vegas, United States, 2016.
  • Cochair, RoLoD International Workshop on Robust Local Descriptors for Computer Vision, ACCV, Singapore, 2014.

Thesis supervisions

PhD Students
  • Zhuo Su (Full time PhD student), Yawen Cui (Full time PhD student), Mohammad Tavakolian (Full time PhD student), Xiaoting Wu (Full time PhD student), Linpu Fang (Visiting PhD student), Changchong Sheng (Visiting PhD student), Wanxia Deng (Visiting PhD student)