Lei Wang's Homepage - Publication

Selected publications (a full list of publications)

Book chapters

1. L. Wang, J. Gall, T. Chin, I. Sato and R. Chellappa, Proceedings of Computer Vision–ACCV 2022: 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022.
2. B. Yu, Y. Wang, L. Wang, D. Shen, and L. Zhou, Medical Image Synthesis via Deep Learning, Deep Learning in Medical Image Analysis (ISBN-13: 978-3030331276, edited by Gobert Lee and Hiroshi Fujita), 2020
3. L. Wang, L. Liu, L. Zhou, and K. Chan, Application of SVMs to the Bag-of-Features Model: A Kernel Perspective, book Support Vector Machines Application, published by Springer in January 2014.
4. L. Zhou, L. Wang, L. Liu, P. Ogunbona, and D. Shen, Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination, book Support Vector Machines Application, published by Springer in January 2014.

Journal publications

1. Z. Wang, L. Liu, L. Wang and L. Zhou, "R2GenGPT: Radiology Report Generation with Frozen LLMs," Meta-Radiology, Available online 15 November, 2023.
2. L. Xin, W. Yang, L. Wang and M. Yang, "Selective Contrastive Learning for Unpaired Multi-View Clustering," IEEE Transactions on Neural Networks and Learning Systems, November 2023.
3. B. Shi, W. Li, J. Huo, P. Zhu, L. Wang and Y. Gao, "Global-and local-aware feature augmentation with semantic orthogonality for few-shot image classification," Pattern Recognition, Volume 142, October 2023.
4. W. Li, Z. Wang, X. Yang, C. Dong, P. Tian, T. Qin, J. Huo, Y. Shi, L. Wang, Y. Gao and J. Luo, “LibFewShot: A Comprehensive Library for Few-shot Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), Accepted in September 2023.
5. L. Qi, J. Liu, L. Wang, Y. Shi and X. Geng, "Unsupervised generalizable multi-source person re-identification: A Domain-specific adaptive framework," Pattern Recognition, Volume 140, August 2023.
6. J. Zhang, Z. Zhang, L. Wang, L. Zhou, X. Zhang, M. Liu and W. Wu, "Kernel-based Feature Aggregation Framework in Point Cloud Networks," Pattern Recognition, Available online 20 February 2023.
7. Y. Duan, Z. Zhao, L. Qi, L. Wang, L. Zhou, Y. Shi, and Y. Gao, "MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization", IEEE Transactions. on Neural Networks and Learning Systems, Data of Publication: 19 December 2022.
8. W. Li, L. Wang, X. Zhang, L. Qi, J. Huo, Y. Gao and J. Luo, "Defensive Few-shot Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in October 2022.
9. S. Chen, L. Wang, Z. Hong and X. Yang, "Domain Generalization by Joint-Product Distribution Alignment," Pattern Recognition, Accepted in October 2022.
10. W. Li, Y. Liu, J. Huo, Y. Shi, Y. Gao, L. Wang and J. Luo, "A Multilayer Framework for Online Metric Learning," IEEE Transactions on Neural Networks and Learning Systems, Accepted in October 2022.
11. L. Qi , L. Wang, Y. Shi and X. Geng, "A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification," IEEE Transactions on Multimedia, Accepted in June 2022.
12. J. Zhang, L. Zhou, L. Wang, M. Liu, and D. Shen, "Diffusion Kernel Attention Network for Brain Disorder Classification", IEEE Transactions on Medical Imaging, Accepted in April 2022.
13. Z. Wang, H. Han, L. Wang, X. Li, and L. Zhou, "Automated Radiographic Report Generation Purely on Transformer: A Multi-criteria Supervised Approach", IEEE Transactions on Medical Imaging, Accepted in April 2022.
14. Y. Shi, C. Zu, M. Hong, L. Zhou, L. Wang, X. Wu, J. Zhou, D. Zhang, and Y. Wang, "ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease", Pattern Recognition, Accepted 30 January 2022.
15. Z. Zhang, L. Wang, Y. Wang, L. Zhou, J. Zhang and F. Chen, Dataset-driven Unsupervised Object Discovery for Region-based Instance Image Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in December 2021.
16. L. Qi, L. Wang, J. Huo, Y. Shi, X. Geng, and Y. Gao, Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification, IEEE Transactions on Circuits and Systems for Video Technology, Accepted in July 2021.
17. B. Yu, L. Zhou, L. Wang, W. Yang, M. Yang, P. Bourgeat, and J. Fripp, SA-LuT-Nets: Learning Sample-adaptive Intensity Lookup Table for Brain Tumor Segmentation, IEEE Transactions on Medical Imaging, Accepted in February 2021.
18. X. Zhang, L. Wang, and Y. Su, "Visual Place Recognition: A Survey from Deep Learning Perspective", Pattern Recognition, Accepted in November 2020.
19. J. Zhang, L. Wang, L. Zhou, and W. Li, “Beyond Covariance: SICE and Kernel based Visual Feature Representation”, International Journal of Computer Vision, Accepted in August 2020.
20. S. Rahman, L. Wang, C. Sun, and L. Zhou, Deep Learning Based HEp-2 Image Classification: A Comprehensive Review, Medical Image Analysis, Accepted in June 2020.
21. Y. Li, L. Wang, W. Zheng, Y. Zong, L. Qi, Z. Cui, T. Zhang, and T. Song, A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition, IEEE Transactions on Cognitive and Developmental Systems, Accepted in May 2020.
22. L. Qi, L. Wang, J. Huo, Y. Shi, and Y. Gao, Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification, IEEE Transactions on Circuits and Systems for Video Technology, Accepted in March 2020.
23. J. Huang, L. Zhou, L. Wang, and D. Zhang, Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis, IEEE Transactions on Medical Imaging, Accepted in February 2020.
24. B. Yu, L. Zhou, L. Wang, Y. Shi, J. Fripp, and P. Bourgeat, Sample-adaptive GANs: Linking Global and Local Mappings for Cross-modality MR Image Synthesis, IEEE Transactions on Medical Imaging, Accepted in January 2020.
25. Y. Li, W. Zheng, L. Wang, Y. Zong, and Z. Cui, From Regional to Global Brain: A Novel Hierarchical Spatial-Temporal Neural Network Model for EEG Emotion Recognition, IEEE Transactions on Affective Computing, First online on 14 June 2019.
26. B. Yu, L. Zhou, L. Wang, Y. Shi, J. Fripp, and P. Bourgeat, Ea-GANs: Edge-aware Generative Adversarial Networks for Cross-modality MR Image Synthesis, IEEE Transactions on Medical Imaging, Accepted in January 2019.
27. X. Liu, L. Wang, X. Zhu, M. Li, E. Zhu, T. Liu, L. Liu, Y. Dou, and J. Yin, Absent Multiple Kernel Learning Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in January 2019.
28. X. Liu, X. Zhu, M. Li, L. Wang, E. Zhu, T. Liu, M. Kloft, D. Shen, J. Yin and W. Gao, Multiple Kernel k-means with Incomplete Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in January 2019.
29. Z. Gao, L. Wang, and L. Zhou, A Probabilistic Approach to Cross-region Matching based Image Retrieval, IEEE Transactions on Image Processing, 28(3):1191-1204, March 2019.
30. Y. Wang, L. Zhou, B. Yu, L. Wang, Z. Chen, D. S. Lalush, W. Lin, X. W, J. Zhou, and D. Shen, 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis, IEEE Transactions on Medical Imaging, Accepted in November 2018.
31. X. Liu, X. Zhu, M. Li, L. Wang, C. Tang, J. Yin, D. Shen, H. Wang, and W. Gao, Late Fusion Incomplete Multi-view Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in October 2018.
32. Y. Wang, B. Yu, L. Wang, C. Zu, D.S. Lalush, W. Lin, X. Wu, J. Zhou, D. Shen, and L. Zhou, 3D Conditional Generative Adversarial Networks for High-quality PET Image Estimation at Low Dose, NeuroImage, 174:550-562, July 2018.
33. W. Yang, Y. Shi, Y. Gao, L. Wang, and M. Yang, Incomplete-Data Oriented Multiview Dimension Reduction via Sparse Low-Rank Representation, IEEE Transactions on Neural Networks and Learning Systems, Date of Publication: 17 May 2018.
34. W. Li, Y. Gao, L. Wang, L. Zhou, J. Huo, and Y. Shi, OPML: A One-Pass Closed-Form Solution for Online Metric Learning, Pattern Recognition, Volume 75, March 2018, Pages 302-314.
35. H. Tian, W. Li, P. Ogunbona, and L. Wang, Detection and Separation of Smoke From Single Image Frames, IEEE Transactions on Image Processing, 27(3), 1164 - 1177, March 2018.
36. L. Liu, P. Wang, C. Shen, L. Wang, A .Van den Hengel, C. Wang, and H. Shen, Compositional Model based Fisher Vector Coding for Image Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2335 – 2348, Dec 2017.
37. S. Huang, J. Zhang, D. Schonfeld, L. Wang, and X. Hua, Two-Stage Friend Recommendation Based on Network Alignment and Series-Expansion of Probabilistic Topic Model, IEEE Transactions on Multimedia, Accepted in Dec 2016.
38. J. Zhang, L. Zhou and L. Wang, Subject-adaptive Integration of Multiple SICE Brain Networks with Different Sparsity, Pattern Recognition, Accepted in Sep 2016.
39. Z. Gao, L. Wang, L. Zhou, and J. Zhang, HEp-2 Cell Image Classification with Deep Convolutional Neural Networks, IEEE Journal on Biomedical and Health Informatics (originally IEEE Transactions on Information Technology in Biomedicine), Accepted in Jan 2016.
40. L. Zhou, L. Wang, L. Liu, P. Ogunbona, and D. Shen, Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in Nov 2015.
41. L. Wang, L. Liu, and L. Zhou, A Graph-embedding Approach to Hierarchical Visual Word Mergence, IEEE Transactions on Neural Networks and Learning Systems, Accepted in Dec 2015.
42. S. Huang, J. Zhang, L. Wang, and X. Hua, Social Friend Recommendation Based on Multiple Network Correlation, IEEE Transactions on Multimedia, Accepted in Dec 2015.
43. H. Ni, L. Zhou, X. Ning, and L. Wang, Exploring Multifractal-based Features for Mild Alzheimer’s Disease Classification, Magnetic Resonance in Medicine, Accepted in July 2015.
44. J. Zhang, L. Wang, L. Zhou, and W. Li, Learning Discriminative Stein Kernel for SPD Matrices and Its Applications, IEEE Transactions on Neural Networks and Learning Systems, Accepted in May 2015.
45. L. Liu, L. Wang, and C. Shen, A Generalized Probabilistic Framework for Compact Codebook Generation,IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in April 2015.
46. C. Wang, L. Wang, and L. Liu, Density Maximization for Improving Graph Matching with Its Applications, IEEE Transactions on Image Processing, Accepted in March 2015.
47. J. Zhang, L. Zhou, L. Wang, and W. Li, Functional Brain Network Classification With Compact Representation of SICE Matrices, IEEE Transactions on Biomedical Engineering, Accepted in January 2015.
48. X. Liu, L. Zhou, L. Wang, J. Zhang, J. Yin and D. Shen, An Efficient Radius-incorporated MKL Algorithm for Alzheimer’s Disease Prediction, Pattern Recognition, Accepted in December 2014.
49. X. Liu, L. Wang, J. Zhang, J. Yin and H. Liu. Global and Local Structure Preservation for Feature Selection, IEEE Transactions on Neural Networks and Learning Systems, 25(6):1083-1095, June 2014.
50. L. Liu and L. Wang. HEp-2 Cell Image Classification with Multiple Linear Descriptors, Pattern Recognition, 47(7):2400-2408, July 2014.
51. H. Tian, W. Li, L. Wang and P. Ogunbona. Smoke Detection in Video: An Image Separation Approach, International Journal of Computer Vision, 106(2):192-209, January 2014.
52. L. Wang, L. Zhou, C. Shen, L. Liu and H. Liu. A Hierarchical Word-merging Algorithm with Class Separability Measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3):417-435, March 2014.
53. C. Shen, J. Kim, F. Liu, L. Wang, and A .Van den Hengel, Efficient Dual Approach to Distance Metric Learning, IEEE Transactions on Neural Networks and Learning Systems, 25(2):394-406 February 2014.
54. X. Liu, L. Wang, J. Yin, E. Zhu and J. Zhang, An Efficient Approach to Integrating Radius Information into Multiple Kernel Learning, IEEE Transactions on Cybernetics, 43(2):557-569, April 2013.
55. Z. Zhao, L. Wang, H. Liu and J. Ye. On Similarity Preserving Feature Selection. IEEE Transactions on Knowledge and Data Engineering, 25(3):619-632, March 2013.
56. X. Liu, J. Yin, L. Wang, L. Liu, J. Liu, C. Hou and J. Zhang, An Adaptive Approach to Learning Optimal Neighbourhood Kernels, IEEE Transactions on Cybernetics, 43(1):371-384, February 2013.
57. C. Shen, J. Kim, L. Wang, and A .Van den Hengel. Positive Semidefinite Metric Learning Using Boosting-like Algorithms. Journal of Machine Learning Research, 13:1007-1036, 2012.
58. L. Zhou, L. Wang, and C. Shen. Feature Selection with Redundancy-constrained Class Separability. IEEE Transactions on Neural Networks, 21(5):853-858, May 2010.
59. C. Shen, J. Kim, and L. Wang. Scalable Large-margin Mahalanobis Distance Metric Learning. IEEE Transactions on Neural Networks, 21(9):1524-1530, September 2010.
60. L. Zhou, R. Hartley, L. Wang, P. Lieby and N. Barnes. Identifying Anatomical Shape Difference by Regularized Discriminative Direction. IEEE Transactions on Medical Imaging, 28(6):937-950, June 2009.
61. L. Wang, Feature Selection with Kernel Class Separability, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(9):1534–1546, September, 2008.
62. L. Wang, K. L. Chan, P. Xue and L. Zhou. A Kernel-induced Space Selection Approach to Model Selection of KLDA. IEEE Transactions on Neural Networks, 19(12):2116-2131, December 2008.
63. L. Wang, K. L. Chan, and P. Xue. Two criteria for Model Selection of Multi-class Support Vector Machines.IEEE Transactions on Systems, Man and Cybernetics, Part B, 38(6):1432-1448, December 2008.
64. H. Kong, L. Wang, E. K. Teoh, X. Li, J.-G. Wang, and R. Venkateswarlu. Generalized 2D principal component analysis for face image representation and recognition. Neural Networks, 18(5-6):585–594, July-August 2005.
65. L. Wang, K. L. Chan, and P. Xue. A criterion for optimizing kernel parameters in KBDA for image retrieval.IEEE Transactions on Systems, Man and Cybernetics, Part B, 35(3):556–562, June 2005.

Conference publications

1. M. Sun, L. Huai, T. Liu, Z. Shangguan, L. Wang, J. Huo, Y. Gao, W. Li, and X. Jiang, "Learning Partial Correlation based Deep Visual Representation for Image Classification," The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2024.
2. Z. Zhang, L. Wang, L. Zhou, and P. Koniusz, "Learning Spatial-context-aware Global Visual Feature Representation for Instance Image Retrieval," International Conference on Computer Vision (ICCV), October, 2023
3. G. Gui, Z. Zhao, L. Qi, L. Zhou, L. Wang, and Y. Shi, “Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning,” International Conference on Computer Vision (ICCV), October, 2023
4. Y. Duan, Z. Zhao, L. Qi, L. Zhou, L. Wang, and Y. Shi, "Class Transition Tracking Based Pseudo-Rectifying Guidance for Semi-supervised Learning with Non-random Missing Labels," International Conference on Computer Vision (ICCV), October, 2023
5. B. P. Voutharoja, L. Wang, and L. Zhou, "Automatic Radiology Report Generation by Learning with Increasingly Hard Negatives", European Conference on Artificial Intelligence (ECAI), September-October 2023
6. S. Rahman, P. Koniusz, L. Wang, L. Zhou, P. Moghadam and C. Sun, "Learning Partial Correlation based Deep Visual Representation for Image Classification," The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023.
7. Z. Wang, L. Liu, L. Wang and L. Zhou, “METransformer: Radiology Report Generation by Transformer with Multiple Expert Learners,” The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023.
8. Y. Ding, L. Wang, B. Liang, S. Liang, Y. Wang and Fang Chen, "Domain Generalization by Learning and Removing Domain-specific Features," The Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), November 2022.
9. G. Gui, Z. Zhao, L. Qi, L. Zhou, L. Wang and Y. Shi, "Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class," The Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), November 2022.
10. Z. Wang, M. Tang, L. Wang, X. Li, and L. Zhou, "A Medical Semantic-Assisted Transformer for Radiographic Report Generation," International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), September 2022.
11. Y. Duan, L. Qi, L. Wang, L. Zhou, Y. Shi, RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning, European Conference on Computer Vision (ECCV), October 2022.
12. S. Zhang, N. Murray, L. Wang, and P. Koniusz, Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection, European Conference on Computer Vision (ECCV), October 2022.
13. Z. Zhao, L. Zhou, Y. Duan, L. Wang, L. Qi, and Y. Shi, DC-SSL: Addressing Mismatched Class Distribution in Semi-supervised Learning, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
14. S. Zhang, L. Wang, N. Murray, and P. Koniusz, Kernelized Few-shot Object Detection by Integral Aggregation, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
15. Z. Zhao, L. Zhou, L. Wang, Y. Shi, and Y. Gao, LaSSL: Label-Guided Self-Training for Semi-Supervised Learning, The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), February-March 2022 (Oral Presentation).
16. Z. Gu, W. Li, J. Huo, L. Wang, and Y. Gao, LoFGAN: Fusing Local Representations for Few-shot Image Generation, IEEE International Conference on Computer Vision (ICCV), October 2021.
17. C. Yan, G. Pan, L. Wang, J. Jiao, X. Feng, C. Shen, and J. Li, BV-Person: A Large-scale Dataset for Bird-view Person Re-identification, IEEE International Conference on Computer Vision (ICCV), October 2021.
18. S. Huang, W. Yang, L. Wang, L. Zhou, and M. Yang. Few-shot Unsupervised Domain Adaptation with Image-to-Class Sparse Similarity Encoding. In Proceedings of the 29th ACM International Conference on Multimedia (ACMMM), October 2021.
19. Z. Wang, L. Zhou, L. Wang, and X. Li, A Self-boosting Framework for Automated Radiographic Report Generation, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.
20. P. Wang, K. Han, X. Wei, L. Zhang, and L. Wang, Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.
21. S. Rahman, L. Wang, C. Sun, and L. Zhou, ReDro: Efficiently Learning Large-sized SPD Visual Representation, The 16th European Conference on Computer Vision (ECCV), August 2020.
22. B. Yu, L. Zhou, L. Wang, W. Yang, M. Yang, P. Bourgeat, and J. Fripp, Learning Sample-adaptive Intensity Lookup Table for Brain Tumor Segmentation, International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), October 2020.
23. W. Li, L. Wang, J. Huo, Y. Shi, Y. Gao and J. Luo, Asymmetric Distribution Measure for Few-shot Learning, International Joint Conference on Artificial Intelligence (IJCAI), July 2020.
24. P. Tian, Z. Wu, L. Qi, L. Wang, Y. Shi, and Y. Gao, Differentiable Meta-learning Model for Few-shot Semantic Segmentation, The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), February 2020.
25. L. Qi, L. Wang, J. Huo, L. Zhou, Y. Shi and Y. Gao, A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification, IEEE International Conference on Computer Vision (ICCV), October-November 2019.
26. H. Wang, L. Zhou and L. Wang, Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation, IEEE International Conference on Computer Vision (ICCV), October-November 2019. (Code)
27. J. Huang, L. Zhou, D. Zhang and L. Wang, Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network, International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), October 2019 (early accept)
28. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao, and J. Luo., Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
29. W. Li, J. Xu, J. Huo, L. Wang, Y. Gao, and J. Luo., Distribution Consistency based Covariance Metric Networks for Few Shot Learning, The Thirty-third AAAI Conference on Artificial Intelligence (AAAI), January-February 2019.
30. M. Engin, L. Wang, L. Zhou, and X. Liu, DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition, The 15th European Conference on Computer Vision (ECCV), September 2018.
31. Y. Zhao, L. Wang, L. Zhou, Y. Shi, and Y. Gao, Modelling Diffusion Process by Deep Neural Networks for Image Retrieval, British Machine Vision Conference (BMVC), September 2018. (Spotlight)
32. Y. Wang, L. Zhou, L. Wang, B. Yu, C. Zu, D.S. Lalush, W. Lin, X. Wu, J. Zhou, and D. Shen, Locality Adaptive Multi-modality GANs for High-quality PET Image Synthesis, the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018.
33. L. Zhou, L. Wang, J. Zhang, Y. Shi and Y. Gao, Revisiting Distance Metric Learning for SPD Matrix based Visual Representation, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
34. M. Zieba and L. Wang, Training Triplet Networks with GAN, Workshop Track of the 5th International Conference on Learning Representations (ICLR), April 2017.
35. X. Liu, M. Li, L. Wang, Y. Dou, J. Yin, and E. Zhu, Multiple Kernel k-Means with Incomplete Kernels, The Thirty-first AAAI Conference on Artificial Intelligence (AAAI), February 2017.
36. M. Li, X. Liu, L. Wang, Y. Dou, J. Yin, and E. Zhu, Multiple Kernel Clustering with Local Kernel Alignment Maximization, The Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), July 2016.
37. X. Liu, Y. Dou, J. Yin, L. Wang, and E. Zhu, Multiple Kernel k-Means Clustering with Matrix-induced Regularization, The Thirtieth AAAI Conference on Artificial Intelligence (AAAI), February 2016.
38. L. Wang, J. Zhang, L. Zhou, C. Tang and W. Li, Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices, IEEE International Conference on Computer Vision (ICCV), December 2015. (Code)
39. X. Liu, L. Wang, J. Yin, D. Yong and J. Zhang, Absent Multiple Kernel Learning, The Twenty-ninth AAAI Conference on Artificial Intelligence (AAAI), January 2015.
40. L. Liu, C. Shen, L. Wang, A .Van den Hengel and C. Wang. Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors, Proceedings of Advances in Neural Information Processing Systems (NIPS), September 2014.
41. C. Wang, L. Wang and L. Liu. Progressive Mode-seeking for Fast Sparse Feature Matching, In the 13th European Conference on Computer Vision (ECCV), September 2014. (Oral presentation, Code)
42. L. Zhou, L. Wang, L. Liu, P. Ogunbona and D. Shen. Max-margin Based Learning for Discriminative Bayesian Network from Neuroimaging Data, In the 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2014.
43. X. Liu, L. Wang, J. Zhang and J. Yin. Sample-adaptive Multiple Kernel Learning, The Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), July 2014.
44. L. Zhou, L. Wang and P. Ogunbona. Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
45. C. Wang, L. Wang and L. Liu. Improving Graph Matching via Density Maximization, IEEE International Conference on Computer Vision (ICCV), December 2013.
46. L. Liu and L. Wang. A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High-dimensional Visual Data, IEEE International Conference on Computer Vision (ICCV), December 2013.
47. L. Wang, J. Zhang, L. Zhou and W. Li. A Fast Approximate AIB Algorithm for Distributional Word Clustering, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2013.
48. L. Zhou, L. Wang, L. Liu, P. Ogunbona and D. Shen. Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Networks, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2013.
49. L. Liu and L. Wang. What Has My Classifier Learned? Visualizing the Classification Rules of Bag-of-Feature Model by Support Region Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2012.
50. L. Liu and L. Wang. Exploring Latent Class Information for Image Retrieval Using the Bag-of-Feature Model, the 20th ACM International Conference on Multimedia (ACMMM), November 2011.
51. L. Liu, L. Wang, and X. Liu. In Defence of Soft-assignment Coding, IEEE International Conference on Computer Vision (ICCV), November 2011.
52. L. Liu, L. Wang, and C. Shen. A Generalized Probabilistic Framework for Compact Codebook Creation, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2011.
53. C. Shen, J. Kim, and L. Wang. A Scalable Dual Approach to Semidefinite Metric, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2011.
54. L. Zhou, L. Wang, C. Shen, and N. Barnes. Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, In the 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2010.
55. Z. Zhao, L. Wang, and H. Liu. Efficient Spectral Feature Selection with Minimum Redundancy. In Proceedings of Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI), July 2010. (Oral Presentation)
56. Y. Zhang, R. Hartley, and L. Wang. Fast Multi-labelling for Stereo Matching, the 11th European Conference on Computer Vision (ECCV), September 2010.
57. C. Shen, J. Kim, L. Wang, and A .Van den Hengel. Positive semidefinite metric learning with Boosting. Proceedings of Advances in Neural Information Processing Systems (NIPS), December 2009.
58. C. Shen, A. Welsh, and L. Wang. PSDBoost: Matrix-generation linear programming for positive semidefinite matrices learning. Proceedings of Advances in Neural Information Processing Systems (NIPS), December 2008.
59. L. Wang, L. Zhou, and C. Shen. A Fast Algorithm for Creating A Compact and Discriminative Visual Codebook. Lecture Notes in Computer Science, Springer, the 10th European Conference on Computer Vision (ECCV), pages 719-732, October 2008.
60. L. Zhou, R. Hartley, L. Wang, P. Lieby and B. Nick. Regularized Discriminative Direction for Shape Difference Analysis. Lecture Notes in Computer Science, Springer, the 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pages 628-635, September 2008.
61. L. Wang. Toward A Discriminative Codebook: Codeword Selection across Multiresolution. the 2nd Beyond Patches Workshop, in conjunction with International Conference on Computer Vision and Pattern Recognition (CVPR), June 2007. (Oral presentation)
62. L. Wang, X. Li, P. Xue, and K. L. Chan. A novel framework for SVM-based image retrieval on large databases. In Proceedings of the 13th ACM International Conference on Multimedia (ACMMM), pages 487–490, November 2005.
63. L. Wang, Y. Gao, K. L. Chan, P. Xue, and W.-Y. Yau. Retrieval with knowledge-driven kernel design: an approach to improving SVM-based CBIR with relevance feedback. In Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV), pages 1355–1362, October 2005.
64. X. Li, L. Wang, and E. Sung. A study of adaboost with SVM based weak learners. In Proceedings of International Joint Conference on Neural Networks (IJCNN), Québec, Canada, pages 196–201(1), August 2005. (Oral presentation)
65. H. Kong, X. Li, L. Wang, E. K. Teoh, J.-G. Wang, and R. Venkateswarlu. Generalized 2D principal component analysis. In Proceedings of International Joint Conference on Neural Networks (IJCNN), Qu´ebec, Canada, pages 108-113(1), August 2005. (Oral presentation)
66. H. Kong, L. Wang, E. K. Teoh, J.-G. Wang, and R. Venkateswarlu. A framework of 2D fisher discriminant analysis: application to face recognition with small number of training samples. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 1083–1088, June 2005.
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