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Professor Chunhua Shen

Chunhua Shen
Professor
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences

My research mainly focuses on Machine Learning and Computer Vision; the objective is to build a visual system with human-like performance. In particular, I am applying Deep Learning to object detection, text-in-the-wild detection and recognition, semantic pixel labelling, and generic image understanding.

I am leading the Adelaide Machine Learning Group: http://blogs.adelaide.edu.au/machine-learning/

Personal web: http://cs.adelaide.edu.au/~chhshen/

Connect With Me

External Profiles

Professor Chunhua Shen

My research mainly focuses on Machine Learning and Computer Vision; the objective is to build a visual system with human-like performance. In particular, I am applying Deep Learning to object detection, text-in-the-wild detection and recognition, semantic pixel labelling, and generic image understanding.

I am leading the Adelaide Machine Learning Group: http://blogs.adelaide.edu.au/machine-learning/

Personal web: http://cs.adelaide.edu.au/~chhshen/

Eligible to supervise Masters and PhD — email supervisor to discuss availability.

I am a Professor of Computer Science at University of Adelaide, leading the Adelaide Machine Learning Group. I held an ARC Future Fellowship from 2012 to 2016. My research and teaching have been focusing on Statistical Machine Learning and Computer Vision.

These days my team spend most effort on Deep Learning.

I received a PhD degree at University of Adelaide; then worked at the NICTA (formerly National ICT Australia) computer vision program for about six years. From 2006 to 2011, I held an adjunct position at College of Engineering & Computer Science, Australian National University. I moved back to University of Adelaide in 2011.

A list of published papers : List in PDFGoogle scholar, and arXiv.
See http://cs.adelaide.edu.au/~chhshen for more information.

Appointments

Date Position Institution name
2011 Senior Lecturer/Associate Professor/Professor University of Adelaide
2005 - 2011 Researcher/Senior Researcher National ICT Australia

Awards and Achievements

Date Type Title Institution Name Country Amount
2012 Fellowship Australian Research Council Future Fellowship Australia

Education

Date Institution name Country Title
2003 - 2006 University of Adelaide Australia PhD
Nanjing University China Master
Nanjing University China Bachelor

Research Interests

Journals

Year Citation
2018 Wu, Q., Shen, C., Wang, P., Dick, A., & van den Hengel, A. (2018). Image captioning and visual question answering based on attributes and external knowledge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1367-1381.
DOI Scopus1 WoS1
2018 Guo, G., Wang, H., Shen, C., Yan, Y., & Liao, H. (2018). Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression. IEEE Transactions on Multimedia, 20(8), 2073-2085.
DOI
2018 You, M., Zhang, Y., Shen, C., & Zhang, X. (2018). An Extended Filtered Channel Framework for Pedestrian Detection. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1640-1651.
DOI
2018 Zhang, J., Wu, Q., Shen, C., Zhang, J., & Lu, J. (2018). Multi-label Image Classification with Regional Latent Semantic Dependencies. IEEE Transactions on Multimedia, Online(10), 1-13.
DOI
2018 Sheng, B., Shen, C., Lin, G., Li, J., Yang, W., & Sun, C. (2018). Crowd Counting via Weighted VLAD on a Dense Attribute Feature Map. IEEE Transactions on Circuits and Systems for Video Technology, 28(8), 1788-1797.
DOI
2018 Liu, H., Ji, R., Wang, J., & Shen, C. (2018). Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1.
DOI
2018 Liu, F., Lin, G., Qiao, R., & Shen, C. (2018). Structured Learning of Tree Potentials in CRF for Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2631-2637.
DOI Scopus2 WoS2
2018 Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., van den Hengel, A., & Zhang, Y. (2018). Unsupervised Domain Adaptation Using Robust Class-Wise Matching. IEEE Transactions on Circuits and Systems for Video Technology, Online, 1-12.
DOI
2018 Hu, Q., Wang, P., Shen, C., Van Den Hengel, A., & Porikli, F. (2018). Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology, 28(6), 1358-1368.
DOI Scopus3 WoS1
2018 Yao, R., Lin, G., Shen, C., Zhang, Y., & Shi, Q. (2018). Semantics-Aware Visual Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology, Online, 1-14.
DOI
2018 Wei, X., Xie, C., Wu, J., & Shen, C. (2018). Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recognition, 76, 704-714.
DOI Scopus3
2018 Zhang, L., Wei, W., Zhang, Y., Shen, C., van den Hengel, A., & Shi, Q. (2018). Cluster sparsity field: an internal hyperspectral imagery prior for reconstruction. International Journal of Computer Vision, 126(8), 797-821.
DOI Scopus6 WoS6
2018 Li, H., Wang, P., You, M., & Shen, C. (2018). Reading car license plates using deep neural networks. Image and Vision Computing, 72, 14-23.
DOI
2018 Lu, H., Shen, C., Cao, Z., Xiao, Y., & Van Den Hengel, A. (2018). An Embarrassingly Simple Approach to Visual Domain Adaptation. IEEE Transactions on Image Processing, 27(7), 3403-3417.
DOI
2018 Zhuang, N., Yan, Y., Chen, S., Wang, H., & Shen, C. (2018). Multi-label learning based deep transfer neural network for facial attribute classification. Pattern Recognition, 80, 225-240.
DOI Scopus1
2017 Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A. R., & Hengel, A. V. D. (2017). Visual question answering: A survey of methods and datasets.. Computer Vision and Image Understanding, 163, 21-40.
2017 Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2017). Part-Based Robust Tracking Using Online Latent Structured Learning. IEEE Transactions on Circuits and Systems for Video Technology, 27(6), 1235-1248.
DOI Scopus1 WoS2
2017 Cao, Y., Wu, Z., & Shen, C. (2017). Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks. IEEE Transactions on Circuits and Systems for Video Technology, Online, 1-9.
DOI Scopus4
2017 Wang, P., Wu, Q., Shen, C., Dick, A., & Hengel, A. (2017). FVQA: Fact-based Visual Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, Online(10), 1-14.
DOI WoS1
2017 Liu, L., Wang, P., Shen, C., Wang, L., van den Hengel, A., Wang, C., & Shen, H. (2017). Compositional model based Fisher vector coding for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2335-2348.
DOI Scopus6 WoS2
2017 Lu, H., Cao, Z., Xiao, Y., Zhuang, B., & Shen, C. (2017). TasselNet: Counting maize tassels in the wild via local counts regression network. Plant Methods, 13(1), 17 pages.
DOI Scopus2 WoS1 Europe PMC1
2017 Hu, Q., Wang, H., Li, T., & Shen, C. (2017). Deep CNNs with Spatially Weighted Pooling for Fine-Grained Car Recognition. IEEE Transactions on Intelligent Transportation Systems, 18(11), 3147-3156.
DOI Scopus3
2017 Liu, F., Lin, G., & Shen, C. (2017). Discriminative Training of Deep Fully Connected Continuous CRFs with Task-Specific Loss. IEEE Transactions on Image Processing, 26(5), 2127-2136.
DOI Scopus1
2017 Li, Y., Li, W., & Shen, C. (2017). Removal of optically thick clouds from high-resolution satellite imagery using dictionary group learning and interdictionary nonlocal joint sparse coding. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 1870-1882.
DOI
2017 Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & van den Hengel, A. (2017). Visual question answering: a survey of methods and datasets. Computer Vision and Image Understanding, 163, 21-40.
DOI Scopus5 WoS2
2017 Wu, L., Chunhua, S., & Hengel, A. (2017). Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification. Pattern Recognition, 65, 238-250.
DOI Scopus29 WoS17
2017 Wang, P., Shen, C., Van Den Hengel, A., & Torr, P. (2017). Large-scale binary quadratic optimization using semidefinite relaxation and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(3), 470-485.
DOI Scopus1 WoS1
2017 Cao, Y., Shen, C., & Shen, H. (2017). Exploiting depth from single monocular images for object detection and semantic segmentation. IEEE Transactions on Image Processing, 26(2), 836-846.
DOI Scopus4 WoS3 Europe PMC1
2017 Li, Y., Liu, L., Shen, C., & Hengel, A. (2017). Mining Mid-level Visual Patterns with Deep CNN Activations. International Journal of Computer Vision, 121(3), 344-364.
DOI Scopus6 WoS3
2017 Paisitkriangkrai, S., Wu, L., Shen, C., & van den Hengel, A. (2017). Structured learning of metric ensembles with application to person re-identification. Computer Vision and Image Understanding, 156, 51-65.
DOI Scopus2 WoS2
2017 Lin, G., Liu, F., Shen, C., Wu, J., & Shen, H. (2017). Structured Learning of Binary Codes with Column Generation for Optimizing Ranking Measures. International Journal of Computer Vision, 123(2), 287-308.
DOI
2017 Qiao, R., Liu, L., Shen, C., & van den Hengel, A. (2017). Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition. Pattern Recognition, 66, 202-212.
DOI Scopus13 WoS5
2016 Zhao, X., Li, X., Zhang, Z., Shen, C., Zhuang, Y., Gao, L., & Li, X. (2016). Scalable linear visual feature learning via online parallel nonnegative matrix factorization. IEEE Transactions on Neural Networks and Learning Systems, 27(12), 2628-2642.
DOI Scopus6
2016 Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7223-7235.
DOI Scopus16 WoS13
2016 Liu, L., Shen, C., & van den Hengel, A. (2016). Cross-convolutional-layer pooling for image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2305-2313.
DOI Scopus3 WoS4
2016 Liu, F., Shen, C., Reid, I., & Van Den Hengel, A. (2016). Online unsupervised feature learning for visual tracking. Image and Vision Computing, 51, 84-94.
DOI Scopus4 WoS3
2016 Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2016). Pedestrian detection with spatially pooled features and structured ensemble learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(6), 1243-1257.
DOI Scopus33 WoS26 Europe PMC2
2016 Liu, L., Wang, L., & Shen, C. (2016). A generalized probabilistic framework for compact codebook creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 224-237.
DOI Scopus1
2016 Hu, Q., Paisitkriangkrai, S., Shen, C., Van Den Hengel, A., & Porikli, F. (2016). Fast detection of multiple objects in traffic scenes with a common detection framework. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1002-1014.
DOI Scopus17 WoS13
2016 Li, X., Shen, C., Dick, A., Zhang, Z., & Zhuang, Y. (2016). Online metric-weighted linear representations for robust visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), 931-950.
DOI Scopus14 WoS11
2016 Liu, F., Shen, C., Lin, G., & Reid, I. (2016). Learning depth from single monocular images using deep convolutional neural fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(10), 2024-2039.
DOI Scopus74 WoS52 Europe PMC7
2016 Zhang, C., Shen, C., & Shen, T. (2016). Unsupervised Feature Learning for Dense Correspondences Across Scenes. International Journal of Computer Vision, 116(1), 90-107.
DOI Scopus4
2016 Wang, P., Shen, C., van den Hengel, A., & Torr, P. (2016). Efficient semidefinite branch-and-cut for MAP-MRF inference. International Journal of Computer Vision, 117(3), 269-289.
DOI
2016 Wang, S., Lu, J., Gu, X., Shen, C., Xia, R., & Yang, J. (2016). Canonical principal angles correlation analysis for two-view data. Journal of Visual Communication and Image Representation, 35, 209-219.
DOI Scopus1 WoS2
2016 Shen, F., Shen, C., Zhou, X., Yang, Y., & Shen, H. (2016). Face image classification by pooling raw features. Pattern Recognition, 54, 94-103.
DOI Scopus7 WoS18
2016 Li, H., Shen, F., Shen, C., Yang, Y., & Gao, Y. (2016). Face recognition using linear representation ensembles. Pattern Recognition, 59, 72-87.
DOI Scopus7
2016 Wang, P., Cao, Y., Shen, C., Liu, L., & Shen, H. (2016). Temporal pyramid pooling based convolutional neural network for action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 27(99), 1-8.
DOI Scopus5 WoS2
2016 Lin, G., Shen, C., Hengel, A., & Reid, I. (2016). Exploring context with deep structured models for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1352-1366.
DOI Scopus4 WoS1
2015 Harandi, M., Hartley, R., Shen, C., Lovell, B., & Sanderson, C. (2015). Extrinsic methods for coding and dictionary learning on Grassmann manifolds. International Journal of Computer Vision, 114(2-3), 113-136.
DOI Scopus25
2015 Luo, L., Shen, C., Liu, X., & Zhang, C. (2015). A computational model of the short-cut rule for 2D shape decomposition. IEEE Transactions on Image Processing, 24(1), 273-283.
DOI Scopus9
2015 Shen, F., Shen, C., Shi, Q., Van Den Hengel, A., Tang, Z., & Shen, H. (2015). Hashing on nonlinear manifolds. IEEE Transactions on Image Processing, 24(6), 1839-1851.
DOI Scopus73 WoS66 Europe PMC12
2015 Liu, F., Lin, G., & Shen, C. (2015). CRF learning with CNN features for image segmentation. Pattern Recognition, 48(10), 2983-2992.
DOI Scopus53 WoS40
2015 Li, H., Shen, C., Van Den Hengel, A., & Shi, Q. (2015). Worst case linear discriminant analysis as scalable semidefinite feasibility problems. IEEE Transactions on Image Processing, 24(8), 2382-2392.
DOI Scopus3 WoS8
2015 Lin, G., Shen, C., & van den Hengel, A. (2015). Supervised hashing using graph cuts and boosted decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(11), 2317-2331.
DOI Scopus30 WoS21 Europe PMC6
2014 Shen, C., Lin, G., & van den Hengel, A. (2014). StructBoost: boosting methods for predicting structured output variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10), 2089-2103.
DOI Scopus9 WoS3
2014 Wang, L., Zhou, L., Shen, C., Liu, L., & Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 417-435.
DOI Scopus9 WoS6 Europe PMC1
2014 Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). Large-margin learning of compact binary image encodings. IEEE Transactions on Image Processing, 23(9), 4041-4054.
DOI Scopus1 WoS1 Europe PMC1
2014 Shen, C., Kim, J., Liu, F., Wang, L., & van den Hengel, A. (2014). Efficient dual approach to distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, 25(2), 394-406.
DOI Scopus19 WoS16 Europe PMC5
2014 Lu, Y., Wang, L., Lu, J., Yang, J., & Shen, C. (2014). Multiple kernel clustering based on centered kernel alignment. Pattern Recognition, 47(11), 3656-3664.
DOI Scopus26
2014 Paisitkriangkrai, S., Shen, C., & van den Hengel, A. (2014). Asymmetric pruning for learning cascade detectors. IEEE Transactions on Multimedia, 16(5), 1254-1267.
DOI Scopus3 WoS1
2014 Yan, Y., Wang, H., & Shen, C. (2014). Efficient semidefinite spectral clustering via lagrange duality. IEEE Transactions on Image Processing, 23(8), 3522-3534.
DOI Scopus7
2014 Liu, F., Zhou, L., Shen, C., & Yin, J. (2014). Multiple kernel learning in the primal for multimodal Alzheimer's disease classification. IEEE Journal of Biomedical and Health Informatics, 18(3), 984-990.
DOI Scopus36 WoS29 Europe PMC17
2014 Paisitkriangkrai, S., Shen, C., Shi, Q., & van den Hengel, A. (2014). RandomBoost: simplified multiclass boosting through randomization. IEEE Transactions on Neural Networks and Learning Systems, 25(4), 764-779.
DOI Scopus6 WoS7 Europe PMC1
2014 Li, Y., Jia, W., Shen, C., & van den Hengel, A. (2014). Characterness: an indicator of text in the wild. IEEE Transactions on Image Processing, 23(4), 1666-1677.
DOI Scopus45 WoS30 Europe PMC5
2014 Shen, F., Shen, C., Hill, R., Van Den Hengel, A., & Tang, Z. (2014). Fast approximate L∞ minimization: speeding up robust regression. Computational Statistics & Data Analysis, 77, 25-37.
DOI Scopus11 WoS10
2014 Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). A scalable stagewise approach to large-margin multiclass loss-based boosting. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 1002-1013.
DOI Scopus7 WoS8 Europe PMC2
2013 Li, X., Dick, A., Shen, C., Van Den Hengel, A., & Wang, H. (2013). Incremental learning of 3D-DCT compact representations for robust visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4), 863-881.
DOI Scopus64 WoS51 Europe PMC5
2013 Luo, L., Shen, C., Zhang, C., & Van Den Hengel, A. (2013). Shape similarity analysis by self-tuning locally constrained mixed-diffusion. IEEE Transactions on Multimedia, 15(5), 1174-1183.
DOI Scopus12 WoS9
2013 Shen, C., Li, H., & Van Den Hengel, A. (2013). Fully corrective boosting with arbitrary loss and regularization. Neural Networks, 48, 44-58.
DOI Scopus4 WoS4 Europe PMC2
2013 Li, X., Dick, A., Shen, C., Zhang, Z., Van Den Hengel, A., & Wang, H. (2013). Visual tracking with spatio-temporal Dempster-Shafer information fusion. IEEE Transactions on Image Processing, 22(8), 3028-3040.
DOI Scopus19 WoS21 Europe PMC3
2013 Shen, F., Shen, C., Van Den Hengel, A., & Tang, Z. (2013). Approximate least trimmed sum of squares fitting and applications in image analysis. IEEE Transactions on Image Processing, 22(5), 1836-1847.
DOI Scopus23 WoS16
2013 Li, X., Hu, W., Shen, C., Dick, A., & Zhang, Z. (2013). Context-aware hypergraph construction for robust spectral clustering. IEEE Transactions on Knowledge & Data Engineering, In Press(10), 1-9.
DOI Scopus21 WoS19
2013 Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Van Den Hengel, A. (2013). A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology, 4(58), 1-48.
DOI Scopus350 WoS302
2013 Shen, C., Wang, P., Paisitkriangkrai, S., & Van Den Hengel, A. (2013). Training effective node classifiers for cascade classification. International Journal of Computer Vision, 103(3), 326-347.
DOI Scopus17 WoS11
2012 Shen, C., Kim, J., Wang, L., & Van Den Hengel, A. (2012). Positive semidefinite metric learning using boosting-like algorithms. Journal of Machine Learning Research (Print), 13(0), 1007-1036.
Scopus51 WoS33
2012 Wang, P., Shen, C., Barnes, N., & Zheng, H. (2012). Fast and robust object detection using asymmetric totally-corrective boosting. IEEE Transactions on Neural Networks and Learning Systems, 23(1), 33-46.
DOI Scopus26 WoS24 Europe PMC12
2012 Shen, C., Wang, P., Shen, F., & Wang, H. (2012). UBoost: Boosting with the Universum. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4), 825-832.
DOI Scopus31 WoS25 Europe PMC1
2011 Shen, C., Paisitkriangkrai, S., & Zhang, J. (2011). Efficiently learning a detection cascade with sparse eigenvectors. IEEE Transactions on Image Processing, 20(1), 22-35.
DOI Scopus21 WoS17 Europe PMC3
2011 Paisitkriangkrai, S., Shen, C., & Zhang, J. (2011). Incremental training of a detector using online sparse eigendecomposition. IEEE Transactions on Image Processing, 20(1), 213-226.
DOI Scopus10 WoS9 Europe PMC2
2010 Shen, C., Kim, J., & Wang, H. (2010). Generalized kernel-based visual tracking. IEEE Transactions on Circuits and Systems for Video Technology, 20(1), 119-130.
DOI Scopus71 WoS54
2010 Shen, C. (2010). On the dual formulation of boosting algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), 2216-2231.
DOI
2010 Shen, C., & Li, H. (2010). Boosting through optimization of margin distributions. IEEE Transactions on Neural Networks, 21(4), 659-666.
DOI
2010 Shen, C., Kim, J., & Wang, L. (2010). Scalable large-margin Mahalanobis distance metric learning. IEEE Transactions on Neural Networks, 21(9), 1524-1530.
DOI
2010 Zhou, L., Wang, L., & Shen, C. (2010). Feature selection with redundancy-constrained class separability. IEEE Transactions on Neural Networks, 21(5), 853-858.
DOI
2010 Li, H., & Shen, C. (2010). Interactive color image segmentation with linear programming. Machine Vision and Applications, 21(4), 403-412.
DOI
2008 Paisitkriangkrai, S., Shen, C., & Zhang, J. (2008). Performance evaluation of local features in human classification and detection. IET Computer Vision, 2(4), 236-246.
DOI Scopus35 WoS24
2008 Paisitkriangkrai, S., Shen, C., & Zhang, J. (2008). Fast pedestrian detection using a cascade of boosted covariance features. IEEE Transactions on Circuits and Systems for Video Technology, 18(8), 1140-1151.
DOI Scopus100
2008 Shen, C., Li, H., & Brooks, M. (2008). Supervised dimensionality reduction via sequential semidefinite programming. Pattern Recognition, 41(12), 3644-3652.
DOI Scopus21 WoS20
2007 Wang, H., Suter, D., Schindler, K., & Shen, C. (2007). Adaptive object tracking based on an effective appearance filter. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9), 1661-1667.
DOI Scopus161 WoS112 Europe PMC14
2007 Shen, C., Brooks, M., & Van Den Hengel, A. (2007). Fast global kernel density mode seeking: Applications to localization and tracking. IEEE Transactions on Image Processing, 16(5), 1457-1469.
DOI Scopus61 WoS45 Europe PMC3
2004 Lin, Z., Lu, J., Shen, C., Qiu, X., & Xu, B. (2004). Active control of radiation from a piston set in a rigid sphere. Journal of the Acoustical Society of America, 115(6), 2954-2963.
DOI Scopus12 Europe PMC2

Conference Papers

Year Citation
2017 Chen, Z., Jacobson, A., Sunderhauf, N., Upcroft, B., Liu, L., Shen, C., . . . Milford, M. (2017). Deep Learning Features at Scale for Visual Place Recognition. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 3223-3230). Online.
DOI Scopus3
2017 Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Explicit knowledge-based reasoning for visual question answering. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1290-1296).
2017 Wei, X., Zhang, C., Li, Y., Xie, C., Wu, J., Shen, C., & Zhou, Z. (2017). Deep descriptor Transforming for image co-localization. In IJCAI International Joint Conference on Artificial Intelligence (pp. 3048-3054).
2017 Shen, T., Lin, G., Shen, C., & Reid, I. (2017). Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation. In Proceedings of International Joint Conference on Artificial Intelligence IJCAI (pp. 2708-2714). Online: International Joint Conferences on Artificial Intelligence.
2017 Li, H., Wang, P., & Shen, C. (2017). Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2017-October (pp. 5248-5256). Venice, ITALY: IEEE.
DOI Scopus1
2017 Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., & Hengel, A. (2017). When Unsupervised Domain Adaptation Meets Tensor Representations. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2017-October (pp. 599-608). Venice, ITALY: IEEE.
DOI Scopus2 WoS1
2017 Wang, P., Wu, Q., Shen, C., & van den Hengel, A. (2017). The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions. In 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) Vol. 2017-January (pp. 3909-3918). Honolulu, HI: IEEE.
DOI Scopus2
2017 Wang, P., Liu, L., Shen, C., Huang, Z., van den Hengel, A., & Shen, H. (2017). Multi-attention network for one shot learning. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 6212-6220). http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8097368: IEEE.
DOI Scopus1 WoS1
2017 Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., & van den Hengel, A. (2017). Sequential Person Recognition in Photo Albums with a Recurrent Network. In 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) Vol. 2017-January (pp. 5660-5668). Honolulu, HI: IEEE.
DOI Scopus2
2017 Chen, Y., Shen, C., Wei, X., Liu, L., & Yang, J. (2017). Adversarial PoseNet: a structure-aware convolutional network for human pose estimation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017) Vol. 2017 (pp. 1221-1230). Piscataway, NJ: IEEE.
DOI Scopus3
2017 Liu, W., Chen, X., Shen, C., Liu, Z., & Yang, J. (2017). Semi-Global Weighted Least Squares in Image Filtering. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2017-October (pp. 5862-5870). Venice, ITALY: IEEE.
DOI Scopus1
2017 Zhuang, B., Liu, L., Shen, C., & Reid, I. (2017). Towards Context-Aware Interaction Recognition for Visual Relationship Detection. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2017-October (pp. 589-598).
DOI
2017 Gong, D., Yang, J., Liu, L., Zhang, Y., Reid, I., Shen, C., . . . Shi, Q. (2017). From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 3806-3815). Piscataway, NJ: IEEE.
DOI Scopus2
2017 Zhuang, B., Liu, L., Li, Y., Shen, C., & Reid, I. (2017). Attend in groups: a weakly-supervised deep learning framework for learning from web data. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 2915-2924). Piscataway, NJ: IEEE.
DOI Scopus1
2017 Lin, G., Milan, A., Shen, C., & Reid, I. (2017). RefineNet: Multi-Path Refinement Networks with Identity Mappings for High-Resolution Semantic Segmentation. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 Vol. 2017-January (pp. 5168-5177). Honolulu, USA.
DOI Scopus28
2017 McMahon, S., Shen, T., Sünderhauf, N., Reid, I., Shen, C., & Milford, M. (2017). Auxiliary tasks to improve trip hazard affordance detection on construction sites. In Australasian Conference on Robotics and Automation, ACRA Vol. 2017-December (pp. 124-132). Online: Australasian Robotics and Automation Association.
2016 Mao, X., Shen, C., & Yang, Y. (2016). Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In Advances in Neural Information Processing Systems. 30th Annual Conference on Neural Information Processing Systems, NIPS 2016 (pp. 2810-2818). Online: Neural information processing systems foundation.
Scopus60
2016 Wang, P., Liu, L., Shen, C., Huang, Z., Van Den Hengel, A., & Shen, H. (2016). What's wrong with that object? Identifying images of unusual objects by modelling the detection score distribution. In Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-January (pp. 1573-1581). Las Vegas, NV: IEEE.
DOI Scopus3 WoS1
2016 Wu, Q., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2016). Ask me anything: free-form visual question answering based on knowledge from external sources. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-January (pp. 4622-4630). Las Vegas, NV: IEEE.
DOI Scopus37 WoS13
2016 Qiao, R., Liu, L., Shen, C., & van den Hengel, A. (2016). Less is more: zero-shot learning from online textual documents with noise suppression. In Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-January (pp. 2249-2257). Las Vegas, NV: IEEE.
DOI Scopus19 WoS10
2016 Zhuang, B., Lin, G., Shen, C., & Reid, I. (2016). Fast training of triplet-based deep binary embedding networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-January (pp. 5955-5964). Las Vegas, NV: IEEE.
DOI Scopus16
2016 Lin, G., Shen, C., Van Den Hengel, A., & Reid, I. (2016). Efficient piecewise training of deep structured models for semantic segmentation. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-January (pp. 3194-3203). Las Vegas, NV: IEEE.
DOI Scopus117 WoS2
2016 Wu, Q., Shen, C., Liu, L., Dick, A., & Van Den Hengel, A. (2016). What value do explicit high level concepts have in vision to language problems?. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-January (pp. 203-212). Las Vegas, NV: IEEE.
DOI Scopus67 WoS22
2016 Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Cluster sparsity field for hyperspectral imagery denoising. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9909 (pp. 631-647). Amsterdam, Netherlands: Springer International Publishing AG.
DOI Scopus7 WoS6
2016 Li, Y., Liu, L., Shen, C., & van den Hengel, A. (2016). Image co-localization by mimicking a good detector’s confidence score distribution. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision, Part II Vol. 9906 LNCS (pp. 19-34). Amsterdam, Netherlands: Springer International Publishing.
DOI Scopus3 WoS2
2016 Zhang, J., Zhang, J., Lu, J., Shen, C., Curr, K., Phua, R., . . . Edmonds, E. (2016). SLNSW-UTS: a historical image dataset for image multi-labeling and retrieval. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (pp. 1-6). Gold Coast, Qld: IEEE.
DOI
2015 Zhang, L., Wei, W., Zhang, Y., Li, F., Shen, C., & Shi, Q. (2015). Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 3550-3558). Santiago, Chile: IEEE.
DOI Scopus9 WoS10
2015 Lin, G., Shen, C., Reid, I., & Van Den Hengel, A. (2015). Deeply learning the messages in message passing inference. In Advances in Neural Information Processing Systems Vol. 2015-January (pp. 361-369).
Scopus13
2015 Shen, F., Shen, C., Liu, W., & Shen, H. (2015). Supervised discrete hashing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 37-45). Boston, MA.
DOI Scopus259
2015 Li, Y., Liu, L., Shen, C., & Van Den Hengel, A. (2015). Mid-level deep pattern mining. In Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 971-980). Boston, MA: IEEE.
DOI Scopus43 WoS22
2015 Wang, P., Shen, C., & Van Den Hengel, A. (2015). Efficient SDP inference for fully-connected CRFs based on low-rank decomposition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 3222-3231). Boston, MA: IEEE.
DOI Scopus5
2015 Tan, M., Shi, Q., Van Den Hengel, A., Shen, C., Gao, J., Hu, F., & Zhang, Z. (2015). Learning graph structure for multi-label image classification via clique generation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 4100-4109). Boston, MA: IEEE.
DOI Scopus7 WoS2
2015 Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2015). Learning to rank in person re-identification with metric ensembles. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 1846-1855). Boston, MA: IEEE.
DOI Scopus135 WoS74
2015 Liu, F., Shen, C., & Lin, G. (2015). Deep convolutional neural fields for depth estimation from a single image. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 5162-5170). Boston, MA: IEEE.
DOI Scopus139
2015 Liu, L., Shen, C., & van den Hengel, A. (2015). The treasure beneath convolutional layers: cross-convolutional-layer pooling for image classification. In Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 4749-4757). Boston, MA: IEEE.
DOI Scopus70 WoS34
2015 Li, B., Shen, C., Dai, Y., Van Den Hengel, A., & He, M. (2015). Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 1119-1127). Boston, MA: IEEE.
DOI Scopus61 WoS30
2015 Milford, M., Lowry, S., Sunderhauf, N., Shirazi, S., Pepperell, E., Upcroft, B., . . . Reid, I. (2015). Sequence searching with deep-learnt depth for condition-and viewpoint-invariant route-based place recognition. In IEEE Conference on Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2015-October (pp. 18-25). Boston, MA: IEEE.
DOI Scopus13
2014 Liu, L., Shen, C., Wang, L., Van Den Hengel, A., & Wang, C. (2014). Encoding high dimensional local features by sparse coding based fisher vectors. In Proceedings of the 27th International Conference on Neural Information Processing Systems Vol. 2 (pp. 1143-1151). Online: MIT Press.
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2014 Lin, G., Shen, C., & Wu, J. (2014). Optimizing ranking measures for compact binary code learning. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014: Proceedings, Part III Vol. 8691 LNCS (pp. 613-627). Zurich, Switzerland: Springer International Publishing.
DOI Scopus6
2014 Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). Strengthening the effectiveness of pedestrian detection with spatially pooled features. In Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014: Proceedings, Part IV Vol. 8692 LNCS (pp. 546-561). Zurich, Switzerland: Springer International Publishing.
DOI Scopus64 WoS27
2014 Lin, G., Shen, C., Shi, Q., Van Den Hengel, A., & Suter, D. (2014). Fast supervised hashing with decision trees for high-dimensional data. In Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1971-1978). Columbus, Ohio: IEEE.
DOI Scopus156 WoS57
2013 Harandi, M., Sanderson, C., Shen, C., & Lovell, B. (2013). Dictionary learning and sparse coding on Grassmann manifolds: an extrinsic solution. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 1-9). USA: IEEE Computer Society.
DOI Scopus56
2013 Lin, G., Shen, C., Suter, D., & Van Den Hengel, A. (2013). A general two-step approach to learning-based hashing. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 1-13). USA: IEEE Computer Society.
DOI Scopus85 WoS45
2013 Li, Y., Shen, C., Jia, W., & Van Den Hengel, A. (2013). Leveraging surrounding context for scene text detection. In Proceedings of the IEEE 2013 International Conference on Image Processing, ICIP 2013 (pp. 2264-2268). USA: IEEE.
DOI Scopus12 WoS8
2013 Li, X., Li, Y., Shen, C., Dick, A., & Van Den Hengel, A. (2013). Contextual hypergraph modeling for salient object detection. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 3328-3335). USA: IEEE Computer Society.
DOI Scopus98 WoS69
2013 Lin, G., Shen, C., & Van Den Hengel, A. (2013). Approximate constraint generation for efficient structured boosting. In Proceedings of the 2013 IEEE International Conference on Image Processing (pp. 4287-4291). USA: IEEE.
DOI
2013 Zhang, C., Bastian, J., Shen, C., Van Den Hengel, A., & Shen, T. (2013). Extended depth-of-field via focus stacking and graph cuts. In Proceedings of the 2013 IEEE Conference on Image Processing, ICIP 2013 (pp. 1272-1276). USA: IEEE.
DOI Scopus6 WoS5
2013 Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2013). Efficient pedestrian detection by directly optimizing the partial area under the ROC curve. In Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013 (pp. 1057-1064). USA: IEEE.
DOI Scopus27 WoS9
2013 Li, X., Lin, G., Shen, C., Van Den Hengel, A., & Dick, A. (2013). Learning hash functions using column generation. In Proceedings of the 30th International Conference on Machine Learning, IMLS 2013 (pp. 1-9). online: IMCL.
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2013 Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2013). Part-based visual tracking with online latent structural learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2363-2370). United States of America: IEEE.
DOI Scopus153 WoS102
2013 Wang, Z., Shi, Q., Shen, C., & Van Den Hengel, A. (2013). Bilinear programming for human activity recognition with unknown MRF graphs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1690-1697). United States of America: IEEE.
DOI Scopus13 WoS5
2013 Shen, F., Shen, C., Shi, Q., Van Den Hengel, A., & Tang, Z. (2013). Inductive hashing on manifolds. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1562-1569). United States of America: IEEE.
DOI Scopus121 WoS74
2013 Wang, P., Shen, C., & Van Den Hengel, A. (2013). A fast semidefinite approach to solving binary quadratic problems. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1312-1319). United States: IEEE.
DOI Scopus13 WoS6
2013 Li, X., Shen, C., Dick, A., & Van Den Hengel, A. (2013). Learning compact binary codes for visual tracking. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2419-2426). United States: IEEE.
DOI Scopus63 WoS45
2012 Lin, G., Shen, C., Van Den Hengel, A., & Suter, D. (2012). Fast training of effective multi-class boosting using coordinate descent optimization. In Proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012 Vol. 7725 LNCS (pp. 782-793). Germany: Springer-Verlag.
DOI
2012 Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2012). Robust tracking with weighted online structured learning. In Proceedings of the 2012 European Conference on Computer Vision, ECCV 2012 Vol. 7574 LNCS (pp. 158-172). Germany: Springer-Verlag.
DOI Scopus19 WoS7
2012 Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2012). Sharing features in multi-class boosting via group sparsity. In Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 2128-2135). USA: IEEE.
DOI Scopus6 WoS3
2012 Li, X., Shen, C., Shi, Q., Dick, A., & Van Den Hengel, A. (2012). Non-sparse linear representations for visual tracking with online reservoir metric learning. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 1760-1767). USA: IEEE.
DOI Scopus60 WoS32
2012 Shi, Q., Shen, C., Hill, R., & Van Den Hengel, A. (2012). Is margin preserved after random projection?. In Proceedings of the29th International Conference on Machine Learning, ICML 12 Vol. 1 (pp. 591-598). USA: Omnipress.
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2011 Li, H., Shen, C., & Shi, Q. (2011). Real-time visual tracking using compressive sensing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1305-1312). 978-1-4577-0394-2: IEEE.
DOI Scopus262 WoS159
2011 Wang, P., Shen, C., Barnes, N., Zheng, H., & Ren, Z. (2011). Asymmetric totally-corrective boosting for real-time object detection. In Computer Vision ¿ ACCV 2010, Lecture Notes in Computer Science, Volume 6492 (pp. 176-188). New York: Springer.
DOI
2011 Wang, L., Shen, C., & Hartley, R. (2011). On the optimality of sequential forward feature selection using class separability measure. In Proceedings International Conference on Digital Image Computing: Techniques and Applications (DICTA'11), 2011 (pp. 203-208). USA: IEEE.
DOI Scopus7
2011 Wang, T., He, X., Shen, C., & Barnes, N. (2011). Laplacian margin distribution boosting for learning from sparsely labeled data. In Proceedings of International Conference on on Digital Image Computing: Techniques and Applications (DICTA'11), 2011. (pp. 209-216). USA: IEEE.
DOI
2011 Liu, L., Wang, L., & Shen, C. (2011). A generalized probabilistic framework for compact codebook creation. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (pp. 1537-1544). USA: IEEE.
DOI Scopus9
2011 Shen, C., & Hao, Z. (2011). A direct formulation for totally-corrective multi-class boosting. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (pp. 2585-2592). USA: IEEE.
DOI Scopus18
2011 Shen, C., Kim, J., & Wang, L. (2011). A scalable dual approach to semidefinite metric learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 2601-2608). USA: IEEE.
DOI Scopus23
2011 Park, K., Shen, C., Hao, Z., & Kim, J. (2011). Efficiently learning a distance metric for large margin nearest neighbor classification. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence Vol. 1 (pp. 453-458). online: AAAI Press.
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2011 Paisitkriangkrai, S., Shen, C., & Zhang, J. (2011). Face detection with effective feature extraction. In Proceedings of the 2010 Asian Conference on Computer Vision; Lecture Notes in Computer Science, vol. 6494, no, 3 Vol. 6494 (pp. 460-470). Heidelberger Platz 3 Berlin Germany D-14197: Springer-Verlag Berlin.
DOI Scopus5 WoS1
2011 Li, X., Dick, A., Wang, H., Shen, C., & Van Den Hengel, A. (2011). Graph mode-based contextual kernels for robust SVM tracking. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2011 (pp. 1156-1163). USA: IEEE.
DOI Scopus28 WoS17
2011 Shi, Q., Eriksson, A., Van Den Hengel, A., & Shen, C. (2011). Is face recognition really a compressive sensing problem?. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 553-560). USA: IEEE.
DOI Scopus215 WoS133
2010 Shi, Q., Li, H., & Shen, C. (2010). Rapid face recognition using hashing. In Proceedings of 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2753-2760). ISA: IEEE.
DOI Scopus30
2010 Shen, C., Wang, P., & Li, H. (2010). LACBoost and FisherBoost: optimally building cascade classifiers. In Proc. European Conference on Computer Vision (ECCV'10) (pp. 608-621). NewYork: Springer Berlin Heidelberg.
DOI
2010 Zhou, L., Wang, L., Shen, C., & Barnes, N. (2010). Hippocampal shape classification using redundancy constrained feature selection. In Proc. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'10), Lecture Notes in Computer Science, Volume 6362 (pp. 266-273). New York: Springer.
DOI
2010 Wang, W., Zhang, J., & Shen, C. (2010). Improved human detection and classification in thermal images. In Proceedings of 2010 IEEE 17th International Conference on Image Processing (ICIP 2010) (pp. 2313-2316). Hong Kong: IEEE.
DOI
2010 Wang, P., Shen, C., Zheng, H., & Ren, Z. (2010). Training a multi-exit cascade with linear asymmetric classification for efficient object detection. In Proceedings of 2010 IEEE 17th IEEE International Conference on Image Processing (ICIP 2010) (pp. 61-64). Hong Kong: IEEE.
DOI
2010 Li, H., Wang, P., & Shen, C. (2010). Robust face recognition via accurate face alignment and sparse representation. In Proc. International Conference on on Digital Image Computing: Techniques and Applications (DICTA'10) (pp. 262-269). Online: IEEE.
DOI
2009 Wang, W., Shen, C., Zhang, J., & Paisitkriangkrai, S. (2009). A two-layer night-time vehicle detector. In Proceedings International Conference on Digital Image Computing - Techniques and Applications (DICTA'09), 2009 (pp. 162-167). Online: IEEE.
DOI Scopus20 WoS12
2009 Dai, Y., Li, H., He, M., & Shen, C. (2009). Smooth Approximation of L-infinity-Norm for Multi-view Geometry. In Proceedings of International Conference on Digital Image Computing - Techniques and Applications (DICTA'09), 2009 (pp. 339-346). Online: IEEE.
DOI
2009 Paisitkriangkrai, S., Shen, C., & Zhang, J. (2009). Efficiently training a better visual detector with sparse eigenvectors. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09) (pp. 1-12). Online: IEEE.
DOI
2009 Wang, P., Shen, C., Zheng, H., & Ren, Z. (2009). A variant of the trace quotient formulation for dimensionality reduction. In H. Zha, R. I. Taniguchi, & S. Maybank (Eds.), Proceedings of 9th Asian Conference on Computer Vision 2009 : LNCS 5996 (pp. 277-286). Berlin: Springer-Verlag.
DOI
2009 Kim, J., Shen, C., & Wang, P. (2009). A scalable algorithm for learning a Mahalanobis distance metric. In Proceedings of 9th Asian Conference on Computer Vision (ACCV'09), 2009 (pp. 299-310). New York: Springer Berlin Heidelberg.
DOI
2009 Zhang, J., Paisitkriangkrai, S., & Shen, C. (2009). An overview of fast pedestrian detection: feature selection and cascade framework of boosted features. In IEEE International Converence on Multimedia and Expo (pp. 1566-1567). Online: IEEE.
DOI Scopus1
2009 Shen, C., Kim, J., Wang, L., & Van Den Hengel, A. (2009). Positive Semidefinite Metric Learning with Boosting. In Proceedings of NIPS 2009 (pp. 1651-1660). online: NIPS.
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2008 Wang, L., Zhou, L., & Shen, C. (2008). A fast algorithm for creating a compact and discriminative visual codebook. In Computer Vision - ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part IV (pp. 719-732). New York: Springer.
DOI
2008 Paisitkriangkrai, S., Shen, C., & Zhang, J. (2008). An experimental study on pedestrian classification using local features. In IEEE International Symposium on Circuits and Systems, 2008 (pp. 2741-2744). Online: IEEE.
DOI Scopus9 WoS4
2008 Paisitkriangkrai, S., Shen, C., & Zhang, J. (2008). Real-time pedestrian detection using a boosted multi-layer classifier. In VS 2008 - The Eighth International Workshop on Visual Surveillance, in conjunction with European Conference on Computer Vision (ECCV'08) (pp. 1-8). Marseille, France.
2008 Shen, C., Welsh, A., & Wang, L. (2008). PSDBoost: matrix-generation linear programming for positive semidefinite matrices learning. In Advances in Neural Information Processing Systems 21 (pp. 1473-1480). Vancouver, British Columbia, Canada: Curran Associates, Inc.
2008 Shen, C., Paisitkriangkrai, S., & Zhang, J. (2008). Face detection from few training examples. In Proceedings of IEEE International Conference on Image Processing (ICiP'08) (pp. 2764-2767). Online: IEEE.
DOI Scopus11 WoS8
2008 Lu, Y., Wang, L., Hartley, R., Li, H., & Shen, C. (2008). Multi-view human motion capture with an improved deformation skin model. In Proceedings of International Conference on Digital Image Computing - Techniques and Applications (DICTA'08) (pp. 420-427). Online: IEEE.
DOI
2008 Kim, J., Shen, C., & Wang, L. (2008). Learning cascaded reduced - set SVMs using linear programming. In Digital Image Computing - Techniques and Applications (DICTA'08) (pp. 619-626). Online: IEEE.
DOI
2008 Shen, C., Li, H., & Brooks, M. (2008). Self-calibrating cameras using semidefinite programming. In Proceedings of the International Conference on Digital Image Computing - Techniques and Applications (DICTA'08) (pp. 436-441). Online: IEEE.
DOI
2008 Li, H., & Shen, C. (2008). Boosting the minimum margin: LPBoost vs. AdaBoost. In Proceedings International Conference on Digital Image Computing - Techniques and Applications (DICTA'08) (pp. 533-539). Online: IEEE.
DOI
2007 Paisitkriangkrai, S., Shen, C., & Zhang, J. (2007). An experimental evaluation of local features for pedestrian classification. In 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (pp. 53-60). Online: IEEE Computer Society.
DOI
2007 Shen, C., & Wen, Z. (2007). Color image labelling using linear programming. In Proceedings International Conference on Digital Image Computing - Techniques and Applications (DICTA'07) (pp. 239-244). Online: IEEE.
DOI
2007 Nguyen, Q., Robles-Kelly, A., & Shen, C. (2007). Kernel-based tracking from a probabilistic viewpoint. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'07) (pp. 1-8). Online: IEEE.
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2007 Shen, C., Li, H., & Brooks, M. (2007). Feature extraction using sequential semidefinite programming. In M. J. Bottema (Ed.), Proceedings of DICTA 2007 (pp. 430-437). CDROM: IEEE.
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2007 Shen, C., Li, H., & Brooks, M. (2007). A convex programming approach to the trace quotient problem. In Yasushi Yagi (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4844 LNCS (PART 2) Vol. 4844 LNCS (pp. 227-235). Germany: Springer.
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2006 Shen, C., Li, H., & Brooks, M. (2006). Classification-based likelihood functions for Bayesian tracking. In M. Piccardi, & T. Hintz (Eds.), Proceedings of AVSS 2006 (pp. CDROM1-CDROM6). CDROM: IEEE.
DOI Scopus1
2006 Nguyen, Q., Robles-Kelly, A., & Shen, C. (2006). Enhanced kernel-based tracking for monochromatic and thermographic video. In Proc. IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS'06) (pp. 1-6). Online: IEEE.
DOI
2006 Li, H., & Shen, C. (2006). An LMI approach for reliable PTZ camera self-calibration. In IEEE International Conference on Video and Signal based Surveillance (AVSS'06) (pp. 1-6). Online: IEEE.
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2005 Shen, C., Van Den Hengel, A., & Brooks, M. (2005). Visual tracking via efficient kernel discriminant subspace learning. In C. S. Regazzoni, & F. De Natale (Eds.), Proceedings of the IEEE International Conference on Image Processing Vol. 2 (pp. 1-4). USA: IEEE.
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2005 Shen, C., Brooks, M., & Van Den Hengel, A. (2005). Augmented particle filtering for efficient visual tracking. In C. S. Regazzoni, & F. De Natale (Eds.), Proceedings of the IEEE International Conference on Image Processing Vol. 3 (pp. 1-4). USA: IEEE.
DOI Scopus6
2005 Shen, C., Brooks, M., & Van Den Hengel, A. (2005). Fast global kernel density mode seeking with application to localisation and tracking. In S. Ma, & H. Y. Shum (Eds.), Proceedings of the Tenth IEEE International Conference on Computer Vision Vol. II (pp. 1516-1523). Los Alamitos, California: IEEE.
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2005 Shen, C., & Brooks, M. (2005). Adaptive over-relaxed mean shift. In A. Bouzerdoum, & A. Beghdadi (Eds.), Proceedings of the 8th International Symposium on Signal Processing and its Applications Vol. 2 (pp. 575-578). USA: IEEE.
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2004 Shen, C., Van Den Hengel, A., Dick, A., & Brooks, M. (2004). Enhanced importance sampling: Unscented auxiliary particle filtering for visual tracking. In G. I. Webb, & X. Yu (Eds.), Proceedings of the 17th Australasian Joint Conference on Artificial Intelligence 2004 Vol. 3339 (pp. 180-191). Berlin, Germany: Springer.
DOI Scopus9 WoS5
2004 Shen, C., Van Den Hengel, A., Dick, A., & Brooks, M. (2004). 2D articulated tracking with dynamic Bayesian networks. In D. Wei, H. Wang, Z. Peng, A. Kara, & Y. He (Eds.), Proceedings of the 4th International Conference on Computer and Information Technology 2004 (pp. 1-7). Los Alamitos, California, USA: IEEE.
DOI Scopus8 WoS1
2003 Shen, C., Van Den Hengel, A., & Dick, A. (2003). Probabilistic multiple cue intergration for particle filter based tracking. In C. Sun, H. Talbot, S. Ourselin, & T. Adriaansen (Eds.), Proceedings of the 7th Biennial Australian Pattern Recognition Society Conference - DICTA 2003 (pp. 399-408). Australia: CSIRO.

SA State Government through its Research Consortia Program under the Premier’s Research and Industry Fund
2018 Unlocking complex resources through lean processing, $8.6m

Australian Research Council (ARC) Grants:
2018-2023, "ARC Industrial Transformation Research Hub for Driving Farming Productivity and Disease Prevention", $8.8m 
2016 LIEF, "Computational infrastructure for developing deep machine learning models", $250k
2014-2021 Centre of Excellence, "Australian Centre for Robotic Vision", $20m
2013 LIEF, "Computational infrastructure for machine learning in computer vision", $210k
2013 LP, "Semantic change detection through large-scale learning", $380k
2012 LP, "Scalable classification for massive datasets: randomised algorithms", $510k
2012-2016 Future Fellowship (step 2), "Continuously learning to see", $650k
2010 LIEF, "Accelerating Australia's large scale video surveillance research programmes", $280k

Cooperative Research Centre (CRC) Grants:
2018-2025 Cyber Security Cooperative Research Centre (CRC), $50m
2017-2019 CRC Project, "Intelligent vision, sensing and data fusion for mining and exploration", $4.6m
2014-2018, Data to Decisions CRC Project Grant, ID: 0001038181, "Large scale image classification", $946k
    CIs: Prof. A. van den Hengel, Prof. C. Shen, Dr A. Dick, Dr A. Eriksson, Dr Q. Shi
2014-2018, Data to Decisions CRC Project Grant, ID: 0001038182, "Exploiting contextual cues in large-scale machine learning", $1.15m
    CIs: Prof. A. van den Hengel, Prof. C. Shen, Dr A. Dick, Dr Q. Shi

Cure Brain Cancer Foundation’s Infrastructure Grant:
2018-2020,  “A national brain-organoid based high-throughput platform for personalised drug and genetic screening in glioblastoma", $400k (with Guillermo Gomez et al.; 1 of the 5 CIs)

Check http://cs.adelaide.edu.au/~chhshen/teaching.html for updated information

  Mining Big Data Course code: COMPSCI 4403
  Introduction to Statistical Machine Learning Course code: COMPSCI 4401/7401
  Algorithm and Data Structure Analysis (Small-Group Discovery Experience) Course code: COMPSCI 2201
  Operating Systems Course code: COMPSCI 3004NA/7064NA
  Computer Graphics Course code: COMPSCI 3014NA
  Scientific Computing (Small-Group Discovery Experience) Course code: COMPSCI 1012
  Introduction to Statistical Machine Learning Course code: COMPSCI 7012
  Master of Computing and Innovation Project Course code: COMPSCI 7098
  Foundations of Computer Science Course code: COMPSCI 2202
  Computer Vision Course code: COMPSCI 4022/7022

  

Current Higher Degree by Research Supervision (University of Adelaide)

Date Role Research Topic Program Degree Type Student Load Student Name
2018 Principal Supervisor Semantic Understanding Based on 3D Data Doctor of Philosophy Doctorate Full Time Mr Wei Yin
2018 Principal Supervisor Towards Real-World Application: Fast High-performance Semantic Segmentation Doctor of Philosophy Doctorate Full Time Mr Zhi Tian
2018 Principal Supervisor Robust Landmark Localisation Using Deep Learning Master of Philosophy Master Full Time Mr Xinyu Wang
2018 Principal Supervisor Multi-modality Data Analysis Using Deep Learning Doctor of Philosophy Doctorate Full Time Mr Hu Wang
2018 Principal Supervisor Instance Level Object Segmentation in Video using Deep Learning Doctor of Philosophy Doctorate Full Time Ms Yutong Dai
2017 Co-Supervisor Attention Mechanisms and Methods for Semantic Image Segmentation Doctor of Philosophy Doctorate Full Time Mr Vladimir Nekrasov
2017 Principal Supervisor Text detection and Recognition in the wild Doctor of Philosophy Doctorate Full Time Mr Tong He
2017 Co-Supervisor Intelligent Dialogue Agent for Software Engineering Doctor of Philosophy Doctorate Full Time Mr Hao Chen
2016 Co-Supervisor Long Term Semantic Scene Understanding and Slam Doctor of Philosophy Doctorate Full Time Mr Ming Cai
2015 Principal Supervisor Compressing Convolutional Neural Networks and Applications Doctor of Philosophy Doctorate Full Time Mr Tong Shen

Past Higher Degree by Research Supervision (University of Adelaide)

Date Role Research Topic Program Degree Type Student Load Student Name
2016 - 2017 Principal Supervisor Deep Learning for Fine-Gained Visual Recognition Master of Philosophy Master Full Time Mr Teng Li
2015 - 2017 Principal Supervisor Sketch Image Recognition Using Deep Features Master of Philosophy Master Full Time Miss Yuchao Jiang
2014 - 2018 Principal Supervisor Text Detection and Recognition in Natural Scene Images Doctor of Philosophy Doctorate Full Time Mrs Hui Li
2014 - 2018 Principal Supervisor Towards Efficient Deep Neural Networks with Applications to Visual Recognition Doctor of Philosophy Doctorate Full Time Mr Bohan Zhuang
2014 - 2017 Principal Supervisor Deep Visual Representation for Weakly-supervised and Structured Output Tasks Doctor of Philosophy Doctorate Full Time Mr Yao Li
2014 - 2016 Principal Supervisor Deep Learning for Multi-label Scene Classification Master of Philosophy Master Full Time Mr Junjie Zhang
2013 - 2018 Principal Supervisor Mid-level Representations for Action Recognition and Zero-shot Learning Doctor of Philosophy Doctorate Full Time Mr Ruizhi Qiao
2013 - 2017 Principal Supervisor Dynamic Scene Understanding with Applications to Traffic Monitoring Doctor of Philosophy Doctorate Full Time Mr Qichang Hu
2013 - 2018 Principal Supervisor Deep Learning Based RGB-D Vision Tasks Doctor of Philosophy Doctorate Full Time Mr Yuanzhouhan Cao
2012 - 2014 Principal Supervisor Hypergraph Modeling for Saliency Detection and Beyond Master of Engineering Science Master Full Time Mr Yao Li
2012 - 2014 Principal Supervisor Structured Output Prediction and Binary Code Learning in Computer Vision Doctor of Philosophy Doctorate Full Time Dr Guosheng Lin
2011 - 2015 Principal Supervisor Learning Structured Prediction Models in Computer Vision Doctor of Philosophy Doctorate Full Time Miss Fayao Liu
Position
Professor
Phone
83136745
Fax
8313 4366
Campus
North Terrace
Building
Ingkarni Wardli, floor 5
Org Unit
School of Computer Science

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