Professor Chunhua Shen

Professor

School of Computer Science

Faculty of Engineering, Computer and Mathematical Sciences

Eligible to supervise Masters and PhD, but is currently at capacity - email supervisor to discuss availability.

I am a Professor of Computer Science at The University of Adelaide since 2014. 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.

My Research
Career
Publications
Grants and Funding
Teaching
Supervision
Contact

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 PDF, Google scholar, and arXiv.
See http://cs.adelaide.edu.au/~chhshen for more information.

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Appointments

Date	Position	Institution name
2014	Professor	University of Adelaide
2011 - 2014	Senior Lecturer/Associate Professor	University of Adelaide
2005 - 2011	Researcher/Senior Researcher	National ICT Australia and Australian National University

Awards and Achievements

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

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

Education

Date	Institution name	Country	Title
2003 - 2005	University of Adelaide	Australia	PhD
—	Nanjing University	China	Master
—	Nanjing University	China	Bachelor
—	Australian National University	Australia	MPhil Applied Statistics

Research Interests

Computer Graphics Computer Vision Knowledge Representation and Machine Learning

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Journals

Year	Citation
2020	Zhao, Y., Liu, Y., Shen, C., Gao, Y., & Xiong, S. (2020). MobileFAN: Transferring deep hidden representation for face alignment. Pattern Recognition, 100, 10 pages. DOI
2020	Zhao, Y., Shen, C., Wang, H., & Chen, S. (2020). Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval. IEEE Transactions on Intelligent Transportation Systems, 21(2), 723-734. DOI
2020	Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., & Zhang, Y. (2020). Learning Deep Gradient Descent Optimization for Image Deconvolution. IEEE Transactions on Neural Networks and Learning Systems. DOI
2020	Liu, W., Zhang, P., Chen, X., Shen, C., Huang, X., & Yang, J. (2020). Embedding Bilateral Filter in Least Squares for Efficient Edge-Preserving Image Smoothing. IEEE Transactions on Circuits and Systems for Video Technology, 30(1), 23-35. DOI
2020	Zhang, L., Wang, P., Shen, C., Liu, L., Wei, W., Zhang, Y., & van den Hengel, A. (2020). Adaptive importance learning for improving lightweight image super-resolution network. International Journal of Computer Vision, 128(2), 479-499. DOI
2019	Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 90, 119-133. DOI Scopus53 WoS23
2019	Wang, P., Liu, L., Shen, C., & Shen, H. (2019). Order-aware convolutional pooling for video based action recognition. Pattern Recognition, 91, 357-365. DOI Scopus1
2019	Nekrasov, V., Chen, H., Shen, C., & Reid, I. D. (2019). Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells.. CVPR, 9126-9135.
2019	Li, H., Wang, P., & Shen, C. (2019). Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks. IEEE Transactions on Intelligent Transportation Systems, 20(3), 1126-1136. DOI Scopus18 WoS9
2019	Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Unsupervised domain adaptation using robust class-wise matching. IEEE Transactions on Circuits and Systems for Video Technology, 29(5), 1339-1349. DOI Scopus2 WoS2
2019	Yao, R., Lin, G., Shen, C., Zhang, Y., & Shi, Q. (2019). Semantics-Aware Visual Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology, 29(6), 1687-1700. DOI Scopus2
2019	Liu, H., Ji, R., Wang, J., & Shen, C. (2019). Ordinal constraint binary coding for approximate nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4), 941-955. DOI Scopus8 WoS4
2019	Wei, X., Zhang, C., Wu, J., Shen, C., & Zhou, Z. (2019). Unsupervised object discovery and co-localization by deep descriptor transformation. Pattern Recognition, 88, 113-126. DOI Scopus4 WoS3
2019	Wang, X., Liu, S., Shen, X., Shen, C., & Jia, J. (2019). Associatively segmenting instances and semantics in point clouds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 4091-4100. DOI Scopus1
2019	He, T., Shen, C., Tian, Z., Gong, D., Sun, C., & Yan, Y. (2019). Knowledge adaptation for efficient semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 578-587. DOI Scopus3
2019	Tian, Z., He, T., Shen, C., & Yan, Y. (2019). Decoders matter for semantic segmentation: Data-dependent decoding enables flexible feature aggregation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 3121-3130. DOI Scopus7
2019	Wei, X., Wang, P., Liu, L., Shen, C., & Wu, J. (2019). Piecewise Classifier Mappings: Learning Fine-Grained Learners for Novel Categories with Few Examples. IEEE Transactions on Image Processing, 28(12), 6116-6125. DOI
2019	Zhang, J., Xie, Y., Xia, Y., & Shen, C. (2019). Attention residual learning for skin lesion classification. IEEE Transactions on Medical Imaging, 38(9), 2092-2103. DOI WoS9
2019	Lu, H., Dai, Y., Shen, C., & Xu, S. (2019). Indices matter: Learning to index for deep image matting. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 3265-3274. DOI
2019	Teney, D., Wang, P., Cao, J., Liu, L., Shen, C., & Hengel, A. V. D. (2019). V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices.. CoRR, abs/1907.12271.
2019	Yin, W., Liu, Y., Shen, C., & Yan, Y. (2019). Enforcing geometric constraints of virtual normal for depth prediction. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 5683-5692. DOI Scopus2
2019	Zhang, X., Cao, J., Shen, C., & You, M. (2019). Self-training with progressive augmentation for unsupervised cross-domain person re-identification. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 8221-8230. DOI
2019	Zhang, H., Li, Y., Jiang, Y., Wang, P., Shen, Q., & Shen, C. (2019). Hyperspectral Classification Based on Lightweight 3-D-CNN with Transfer Learning. IEEE Transactions on Geoscience and Remote Sensing, 57(8), 5813-5828. DOI Scopus6 WoS4
2019	Zhang, H., Shen, C., Li, Y., Cao, Y., Liu, Y., & Yan, Y. (2019). Exploiting temporal consistency for real-time video depth estimation. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 1725-1734. DOI Scopus2
2019	Tian, Z., Shen, C., Chen, H., & He, T. (2019). FCOS: Fully convolutional one-stage object detection. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9626-9635. DOI Scopus2
2019	Nekrasov, V., Chen, H., Shen, C., & Reid, I. D. (2019). Architecture Search of Dynamic Cells for Semantic Video Segmentation.. CoRR, abs/1904.02371.
2019	Nekrasov, V., Shen, C., & Reid, I. D. (2019). Template-Based Automatic Search of Compact Semantic Segmentation Architectures.. CoRR, abs/1904.02365.
2019	Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Accurate imagery recovery using a multi-observation patch model. Information Sciences, 501, 724-741. DOI
2019	Zhang, P., Liu, W., Lu, H., & Shen, C. (2019). Salient object detection with lossless feature reflection and weighted structural loss. IEEE Transactions on Image Processing, 28(6), 3048-3060. DOI Scopus8 WoS2
2019	Zhang, J., Wu, Q., Zhang, J., Shen, C., Lu, J., & Wu, Q. (2019). Heritage image annotation via collective knowledge. Pattern Recognition, 93, 204-214. DOI Scopus1
2019	Zhang, T., Lin, G., Cai, J., Shen, T., Shen, C., & Kot, A. (2019). Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation. IEEE Transactions on Multimedia, 21(11), 2930-2941. DOI Scopus3 WoS1
2019	Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2019). Fast neural architecture search of compact semantic segmentation models via auxiliary cells. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 9118-9127. DOI Scopus1
2019	Xiong, H., Cao, Z., Lu, H., Madec, S., Liu, L., & Shen, C. (2019). TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks. Plant Methods, 15(1), 150-1-150-14. DOI
2019	Peng, X., Zhu, H., Feng, J., Shen, C., Zhang, H., & Zhou, J. T. (2019). Deep Clustering With Sample-Assignment Invariance Prior.. IEEE transactions on neural networks and learning systems. DOI
2019	Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Accurate Tensor Completion via Adaptive Low-Rank Representation.. IEEE transactions on neural networks and learning systems. DOI
2019	Yan, Y., Huang, Y., Chen, S., Shen, C., & Wang, H. (2019). Joint Deep Learning of Facial Expression Synthesis and Recognition. IEEE Transactions on Multimedia. DOI
2018	Nekrasov, V., Shen, C., & Reid, I. D. (2018). Light-Weight RefineNet for Real-Time Semantic Segmentation.. CoRR, abs/1810.03272.
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 Scopus8 WoS6
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 Scopus14 WoS6
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 Scopus7 WoS4
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 Scopus4 WoS3
2018	Zhang, J., Wu, Q., Shen, C., Zhang, J., & Lu, J. (2018). Multilabel image classification with regional latent semantic dependencies. IEEE Transactions on Multimedia, 20(10), 2801-2813. DOI Scopus24 WoS9
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 Scopus35 WoS37
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 Scopus22 WoS10
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 Scopus14 WoS7
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 Scopus21 WoS12
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 Scopus48 WoS28
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 Scopus27 WoS19
2018	Cao, Y., Wu, Z., & Shen, C. (2018). Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks. IEEE Transactions on Circuits and Systems for Video Technology, 28(11), 3174-3182. DOI Scopus23 WoS16
2018	Nekrasov, V., Shen, C., & Reid, I. D. (2018). Diagnostics in semantic segmentation. CoRR, abs/1809.10328, 1-16.
2018	Cao, Y., Zhao, T., Xian, K., Shen, C., Cao, Z., & Xu, S. (2018). Monocular Depth Estimation with Augmented Ordinal Depth Relationships. IEEE Transactions on Image Processing. 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 Scopus20 WoS14
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 Scopus24 WoS15 Europe PMC2
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 Scopus29 WoS17
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 Scopus17 WoS8
2017	Wu, L., Chunhua, S., & Van Den Hengel, A. (2017). Deep linear discriminant analysis on fisher networks: a hybrid architecture for person re-identification. Pattern Recognition, 65, 238-250. DOI Scopus98 WoS70
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 Scopus9 WoS7
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 Scopus8 WoS5
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 Scopus4 WoS3
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 Scopus31 WoS20
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 Scopus49 WoS28
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 Scopus13 WoS10
2017	Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2017). FVQA: fact-based Visual Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2413-2427. DOI Scopus14 WoS7
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 Scopus6 WoS4
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 Scopus5 WoS5 Europe PMC1
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 Scopus8 WoS7
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 Scopus19 WoS15 Europe PMC4
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 Scopus32 WoS20
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 Scopus10 WoS7
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 Scopus63 WoS48 Europe PMC4
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 Scopus4 WoS5
2016	Li, H., Shen, F., Shen, C., Yang, Y., & Gao, Y. (2016). Face recognition using linear representation ensembles. Pattern Recognition, 59, 72-87. DOI Scopus14 WoS8
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 Scopus33 WoS23
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 Scopus3 WoS2
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 Scopus34 WoS23
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 Scopus18 WoS16
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 Scopus25 WoS16 Europe PMC2
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 Scopus23 WoS22 Europe PMC1
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 Scopus9 WoS8
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 Scopus30 WoS23
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 Scopus332 WoS185 Europe PMC17
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 Scopus2 WoS1
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 WoS4
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 Scopus39 WoS33
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 Scopus11 WoS12
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 Scopus5 WoS5
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 Scopus52 WoS38 Europe PMC6
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 Scopus97 WoS83 Europe PMC13
2015	Liu, F., Lin, G., & Shen, C. (2015). CRF learning with CNN features for image segmentation. Pattern Recognition, 48(10), 2983-2992. DOI Scopus103 WoS81
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 Scopus10 WoS4
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 Scopus11 WoS7 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 Scopus25 WoS22 Europe PMC6
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 Scopus46 WoS30
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 Scopus4 WoS2
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 Scopus9 WoS8
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 Scopus61 WoS49 Europe PMC21
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 Scopus62 WoS49 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 Scopus12 WoS11
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 Scopus8 WoS9 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 Scopus73 WoS58 Europe PMC6
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 Scopus17 WoS13
2013	Shen, C., Li, H., & Van Den Hengel, A. (2013). Fully corrective boosting with arbitrary loss and regularization. Neural Networks, 48, 44-58. DOI Scopus6 WoS5 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 Scopus25 WoS25 Europe PMC4
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 Scopus25 WoS18
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, 26(10), 1-9. DOI Scopus26 WoS25
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 Scopus491 WoS427
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 Scopus22 WoS17
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. Scopus62 WoS49
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 Scopus30 WoS27 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 Scopus37 WoS32 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 Scopus24 WoS19 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 Scopus12 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 Scopus78 WoS61
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 Scopus38 WoS31
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 Scopus106
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 Scopus170 WoS119 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 Scopus66 WoS48 Europe PMC4
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 Scopus14 Europe PMC2
—	Chen, Y., Shen, C., Chen, H., Wei, X. -S., Liu, L., & Yang, J. (n.d.). Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization.
—	Zhuang, B., Shen, C., & Reid, I. (n.d.). Training Compact Neural Networks with Binary Weights and Low Precision Activations.
—	Wu, Z., Shen, C., & Hengel, A. V. D. (n.d.). Real-time Semantic Image Segmentation via Spatial Sparsity.
—	Deng, R., Zhao, T., Shen, C., & Liu, S. (n.d.). Relative Depth Order Estimation Using Multi-scale Densely Connected Convolutional Networks.
—	Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. V. D. (n.d.). Care about you: towards large-scale human-centric visual relationship detection.
—	Zhuang, B., Liu, L., Shen, C., & Reid, I. (n.d.). Towards Context-aware Interaction Recognition.
—	Qiao, R., Liu, L., Shen, C., & Hengel, A. V. D. (n.d.). Visually Aligned Word Embeddings for Improving Zero-shot Learning.
—	Liu, W., Chen, X., Shen, C., Yu, J., Wu, Q., & Yang, J. (n.d.). Robust Guided Image Filtering.
—	Wang, P., Liu, L., Shen, C., Hengel, A. V. D., & Shen, H. T. (n.d.). Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution.
—	Lin, G., Liu, F., Shen, C., Wu, J., & Shen, H. T. (n.d.). Structured Learning of Binary Codes with Column Generation.
—	Liu, F., & Shen, C. (n.d.). Learning Deep Convolutional Features for MRI Based Alzheimer's Disease Classification.
—	Shen, C., & Liu, F. (n.d.). From Kernel Machines to Ensemble Learning.
—	Shen, F., & Shen, C. (n.d.). Generic Image Classification Approaches Excel on Face Recognition.
—	Shen, C., Paisitkriangkrai, S., & Hengel, A. V. D. (n.d.). A Direct Approach to Multi-class Boosting and Extensions.
—	Shen, C., Li, H., & Barnes, N. (n.d.). Totally Corrective Boosting for Regularized Risk Minimization.
—	Zheng, Y., Shen, C., Hartley, R., & Huang, X. (n.d.). Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns.
—	Hao, Z., Shen, C., Barnes, N., & Wang, B. (n.d.). Totally Corrective Multiclass Boosting with Binary Weak Learners.
—	Li, H., Shen, C., & Shi, Q. (n.d.). Real-time Visual Tracking Using Sparse Representation.
—	Shen, C., Wang, P., & Hengel, A. V. D. (n.d.). Optimally Training a Cascade Classifier.
—	Wu, L., Shen, C., & Hengel, A. V. D. (n.d.). PersonNet: Person Re-identification with Deep Convolutional Neural Networks.
—	Mao, X. -J., Shen, C., & Yang, Y. -B. (n.d.). Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections.
—	Wu, L., Shen, C., & Hengel, A. V. D. (n.d.). Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach.
—	Wu, Z., Shen, C., & Hengel, A. V. D. (n.d.). Bridging Category-level and Instance-level Semantic Image Segmentation.
—	Wu, Z., Shen, C., & Hengel, A. V. D. (n.d.). High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks.
—	Li, H., & Shen, C. (n.d.). Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs.
—	Wang, X., Man, Z., You, M., & Shen, C. (n.d.). Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition.
—	Tian, Z., Chen, H., & Shen, C. (n.d.). DirectPose: Direct End-to-End Multi-Person Pose Estimation.
—	Cao, J., Liu, L., Wang, P., Huang, Z., Shen, C., & Shen, H. T. (n.d.). Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps.
—	Xiong, H., Lu, H., Liu, C., Liu, L., Shen, C., & Cao, Z. (n.d.). From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting.
—	Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., & Yan, Y. (n.d.). BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation.
—	Wang, X., Kong, T., Shen, C., Jiang, Y., & Li, L. (n.d.). SOLO: Segmenting Objects by Locations.
—	Tian, Z., Shen, C., & Chen, H. (n.d.). Conditional Convolutions for Instance Segmentation.
—	Wang, X., Zhang, R., Kong, T., Li, L., & Shen, C. (n.d.). SOLOv2: Dynamic, Faster and Stronger.
—	Pang, G., Yan, C., Shen, C., Hengel, A. V. D., & Bai, X. (n.d.). Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection.
—	Wang, X., Liu, Y., Shen, C., Ng, C. C., Luo, C., Jin, L., . . . Wang, L. (n.d.). On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering.
—	Liu, Y., Shen, C., Yu, C., & Wang, J. (n.d.). Efficient Semantic Video Segmentation with Per-frame Inference.
—	Liu, Y., Chen, H., Shen, C., He, T., Jin, L., & Wang, L. (n.d.). ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network.
—	Yin, W., Wang, X., Shen, C., Liu, Y., Tian, Z., Xu, S., . . . Renyin, D. (n.d.). DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data.
—	Li, H., Wang, P., & Shen, C. (n.d.). Towards End-to-End Text Spotting in Natural Scenes.
—	Zhuang, B., Liu, J., Tan, M., Liu, L., Reid, I., & Shen, C. (n.d.). Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations.
—	Lu, H., Dai, Y., Shen, C., & Xu, S. (n.d.). Index Network.
—	Zhang, H., Li, Y., Chen, H., & Shen, C. (n.d.). Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising.
—	Zhang, X., Zhang, R., Cao, J., Gong, D., You, M., & Shen, C. (n.d.). Part-Guided Attention Learning for Vehicle Re-Identification.
—	Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (n.d.). Structured Binary Neural Networks for Image Recognition.
—	Zhang, H., Li, Y., Wang, P., Liu, Y., & Shen, C. (n.d.). RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks.
—	Zhuang, B., Liu, L., Tan, M., Shen, C., & Reid, I. (n.d.). Training Quantized Neural Networks with a Full-precision Auxiliary Module.

Conference Papers

Year	Citation
2019	Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T., Shen, C., & Reid, I. (2019). Real-time joint semantic segmentation and depth estimation using asymmetric annotations. In Proceedings of the 2019 IEEE International Conference on Robotics and Automation (ICRA) Vol. 2019-May (pp. 7101-7107). Montreal, Canada: IEEE. DOI Scopus2
2019	Li, H., Wang, P., Shen, C., & Zhang, G. (2019). Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition. In THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE (pp. 8610-8617). Honolulu, HI: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. WoS1
2019	Li, R., Xian, K., Shen, C., Cao, Z., Lu, H., & Hang, L. (2019). Deep Attention-Based Classification Network for Robust Depth Prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11364 LNCS (pp. 663-678). Online: Springer Verlag. DOI Scopus2
2019	Li, H., Wang, P., Shen, C., & Van Den Hengel, A. (2019). Visual question answering as reading comprehension. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 6312-6321). online: IEEE. DOI Scopus1
2019	Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2019). Structured binary neural networks for accurate image classification and semantic segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 413-422). online: IEEE. DOI Scopus2
2019	Wei, X., Zhang, C., Liu, L., Shen, C., & Wu, J. (2019). Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification. In Proceedings of the 14th Asian Conference on Computer Vision (ACCV 2018), as published in Lecture Notes in Computer Science Vol. 11362 (pp. 575-591). Switzerland: Springer. DOI
2019	Pang, G., Shen, C., & Van Den Hengel, A. (2019). Deep anomaly detection with deviation networks. In KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 353-362). New York: Association of Computing Machinery. DOI
2019	Cai, M., Shen, C., & Reid, I. (2019). A hybrid probabilistic model for camera relocalization. In British Machine Vision Conference 2018, BMVC 2018 (pp. 1-12). online: BMVC.
2019	Yan, C., Pang, G., Bai, X., Shen, C., Zhou, J., & Hancock, E. (2019). Deep hashing by discriminating hard examples. In MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia (pp. 1535-1542). online: ACM. DOI
2019	Zhang, J., Xie, Y., Zhang, P., Chen, H., Xia, Y., & Shen, C. (2019). Light-weight hybrid convolutional network for liver tumor segmentation. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2019-August (pp. 4271-4277). online: AAAI Press. Scopus1
2019	Xie, Y., Lu, H., Zhang, J., Shen, C., & Xia, Y. (2019). Deep segmentation-emendation model for gland instance segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11764 LNCS (pp. 469-477). Switzerland: Springer. DOI
2019	Yan, Q., Gong, D., Shi, Q., Van Den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019). Attention-guided network for ghost-free high dynamic range imaging. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 1751-1760). online: IEEE. DOI Scopus1
2019	Zhang, J., Wu, Q., Zhang, J., Shen, C., & Lu, J. (2019). Mind your neighbours: Image annotation with metadata neighbourhood graph co-attention networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 2951-2959). online: IEEE. DOI
2018	Wang, X., Xiao, T., Jiang, Y., Shao, S., Sun, J., & Shen, C. (2018). Repulsion Loss: Detecting Pedestrians in a Crowd. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 7774-7783). online: IEEE. DOI Scopus45 WoS16
2018	Shen, T., Lin, G., Shen, C., & Reid, I. (2018). Bootstrapping the Performance of Webly Supervised Semantic Segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1363-1371). online: IEEE. DOI Scopus10 WoS2
2018	Cao, I., Guo, Y., Wu, Q., Shen, C., Huang, J., & Tan, M. (2018). Adversarial learning with local coordinate coding. In 35th International Conference on Machine Learning, ICML 2018 Vol. 2 (pp. 1104-1117). online: PMLR. Scopus3
2018	Zhuang, B., Wu, Q., Shen, C., Reid, I., & Van Den Hengel, A. (2018). HCVRD: A benchmark for large-scale human-centered visual relationship detection. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 7631-7638). New Orleans: Association for the Advancement of Artificial Intelligence. Scopus4
2018	Zhang, J., Wu, Q., Zhang, J., Shen, C., & Lu, J. (2018). Kill two birds with one stone: Weakly-supervised neural network for image annotation and tag refinement. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 7550-7557). New Orleans: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. Scopus1 WoS1
2018	Chen, Y., Tai, Y., Liu, X., Shen, C., & Yang, J. (2018). FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2492-2501). Salt Lake City, UT: IEEE. DOI Scopus49 WoS13
2018	Wu, Q., Wang, P., Shen, C., Reid, I., & Hengel, A. (2018). Are you talking to me? Reasoned visual dialog generation through adversarial learning. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)) (pp. 6106-6115). Salt Lake City, UT: IEEE. DOI Scopus13 WoS2
2018	Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., . . . Yang, M. (2018). VITAL: VIsual Tracking via Adversarial Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 8990-8999). online: IEEE. DOI Scopus78 WoS35
2018	Xian, K., Shen, C., Cao, Z., Lu, H., Xiao, Y., Li, R., & Luo, Z. (2018). Monocular Relative Depth Perception with Web Stereo Data Supervision. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 311-320). online: IEEE. DOI Scopus18 WoS5
2018	He, T., Tian, Z., Huang, W., Shen, C., Qiao, Y., & Sun, C. (2018). An end-to-end textspotter with explicit alignment and attention. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)) (pp. 5020-5029). Salt Lake City, UT: IEEE. DOI Scopus31 WoS10
2018	Nekrasov, V., Shen, C., & Reid, I. D. (2018). Light-Weight RefineNet for Real-Time Semantic Segmentation. In BMVC (pp. 1-15). Northumbria University, Newcastle upon Tyne: BMVA Press. Scopus7
2018	Dong, L., Gan, Y., Mao, X., Yang, Y., & Shen, C. (2018). Learning Deep Representations Using Convolutional Auto-Encoders with Symmetric Skip Connections. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings Vol. 2018-April (pp. 3006-3010). online: IEEE. DOI
2018	Zhang, J., Wu, Q., Shen, C., Zhang, J., Lu, J., & van den Hengel, A. (2018). Goal-oriented visual question generation via intermediate rewards. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018: Proceedings, Part V Vol. Lecture Notes in Computer Science; vol. 11209 (pp. 189-204). Munich: Springer. DOI Scopus1
2018	Zhang, P., Liu, W., Lu, H., & Shen, C. (2018). Salient object detection by lossless feature reflection. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2018-July (pp. 1149-1155). online: ACM. Scopus11
2018	Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2018). Towards Effective Low-Bitwidth Convolutional Neural Networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 7920-7928). DOI Scopus14 WoS5
2018	Zhuang, B., Wu, Q., Shen, C., Reid, I., & van den Hengel, A. (2018). Parallel attention: a unified framework for visual object discovery through dialogs and queries. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 4252-4261). Salt Lake City, UT: IEEE. DOI Scopus8
2018	Ma, C., Shen, C., Dick, A., Wu, Q., Wang, P., Van Den Hengel, A., & Reid, I. (2018). Visual Question Answering with memory-augmented network. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR 20418 (pp. 6975-6984). Salt Lake City, Utah: IEEE. DOI Scopus14 WoS2
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 Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 3909-3918). Honolulu: IEEE. DOI Scopus27 WoS5
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). Online: IEEE. DOI Scopus25 WoS7
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 Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 5660-5668). online: IEEE. DOI Scopus15 WoS3
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 Vol. 2017-January (pp. 5168-5177). online: IEEE. DOI Scopus457 WoS91
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). Online: IEEE. DOI Scopus13 WoS17
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). Online: IEEE. DOI Scopus43 WoS6
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: IEEE. DOI Scopus46
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. Scopus3
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 Scopus27 WoS5
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: 2017 IEEE International Conference on Computer Vision Vol. 2017-October (pp. 599-608). Venice, Italy: IEEE. DOI Scopus19 WoS7
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 Scopus70 WoS25
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 Scopus9 WoS6
2017	Zhuang, B., Liu, L., Shen, C., & Reid, I. (2017). Towards context-aware interaction recognition for visual relationship detection. In Proceedings 2017 IEEE International Conference on Computer Vision ICCV 2017 Vol. 2017-October (pp. 589-598). Venice, Italy: IEEE. DOI Scopus37 WoS13
2017	Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Explicit knowledge-based reasoning for visual question answering. In C. Sierra (Ed.), Proceedings of the twenty-sixth International Joint Conference on Artificial Intelligence (pp. 1290-1296). online: IJCAI. DOI Scopus17
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). USA: AAAI Press. Scopus6
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. Scopus1
2017	Shen, T., Lin, G., Liu, L., Shen, C., & Reid, I. (2017). Weakly supervised semantic segmentation based on web image co-segmentation. In British Machine Vision Conference 2017, BMVC 2017. Scopus9
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 Vol. 29 (pp. 2810-2818). Online: Neural information processing systems foundation. Scopus345 WoS10
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-December (pp. 1573-1581). Las Vegas, NV: IEEE. DOI Scopus6 WoS3
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-December (pp. 4622-4630). Las Vegas, NV: IEEE. DOI Scopus121 WoS50
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-December (pp. 2249-2257). Las Vegas, NV: IEEE. DOI Scopus57 WoS33
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-December (pp. 5955-5964). Las Vegas, NV: IEEE. DOI Scopus55 WoS31
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-December (pp. 3194-3203). Las Vegas, NV: IEEE. DOI Scopus299 WoS8
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-December (pp. 203-212). Las Vegas, NV: IEEE. DOI Scopus210 WoS98
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 Scopus12 WoS10
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 Scopus18 WoS12
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 Scopus2 WoS1
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: IEEE. DOI Scopus519 WoS328
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 Scopus55 WoS31
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 Scopus8 WoS1
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 Scopus19 WoS11
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 Scopus277 WoS161
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 Scopus104 WoS52
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 Scopus209 WoS104
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 Scopus21
2015	Lin, G., Shen, C., Reid, I., & Van Den Hengel, A. (2015). Deeply learning the messages in message passing inference. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: 29th Annual Conference on Neural Information Processing Systems 2015 Vol. 2015-January (pp. 361-369). Montreal: Neural Information Processing Systems. Scopus22
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 Scopus12 WoS12
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 Scopus333 WoS156
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. Scopus51 WoS1
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 Scopus8 WoS8
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 Scopus100 WoS79
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 Scopus253 WoS98
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 Scopus81 WoS59
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 Scopus128 WoS81
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 Scopus14 WoS11
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 Scopus138 WoS96
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 Scopus8 WoS7
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 Scopus34 WoS17
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. Scopus86
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 Scopus178 WoS127
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 Scopus21 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 Scopus160 WoS112
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 Scopus17 WoS7
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 Scopus74 WoS51
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 Scopus23 WoS20
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 Scopus66 WoS39
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. Scopus28
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). Online: IEEE. DOI Scopus293 WoS195
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 Scopus9
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 WoS6
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 Scopus19
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 Scopus24
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. Scopus15
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 Scopus7 WoS2
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 Scopus33 WoS23
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 Scopus245 WoS163
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). USA: IEEE. DOI Scopus34
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 Scopus23 WoS17
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. 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 Scopus2 WoS1
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. Scopus54
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 Scopus10 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 WoS9
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. DOI
2007	Shen, C., Li, H., & Brooks, M. (2007). Feature extraction using sequential semidefinite programming. In M. Bottema (Ed.), Proceedings of DICTA 2007 (pp. 430-437). CDROM: IEEE. DOI Scopus3
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. DOI Scopus10 WoS11
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. DOI
2005	Shen, C., Van Den Hengel, A., & Brooks, M. (2005). Visual tracking via efficient kernel discriminant subspace learning. In C. Regazzoni, & F. De Natale (Eds.), Proceedings of the IEEE International Conference on Image Processing Vol. 2 (pp. 1-4). USA: IEEE. DOI Scopus7
2005	Shen, C., Brooks, M., & Van Den Hengel, A. (2005). Augmented particle filtering for efficient visual tracking. In C. Regazzoni, & F. De Natale (Eds.), Proceedings of the IEEE International Conference on Image Processing Vol. 3 (pp. 1-4). USA: IEEE. DOI Scopus7 WoS3
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. Shum (Eds.), Proceedings of the Tenth IEEE International Conference on Computer Vision Vol. II (pp. 1516-1523). Los Alamitos, California: IEEE. DOI Scopus35 WoS18
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. DOI Scopus5
2004	Shen, C., Van Den Hengel, A., Dick, A., & Brooks, M. (2004). Enhanced importance sampling: Unscented auxiliary particle filtering for visual tracking. In G. 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 WoS3
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.
—	Bian, J. -W., Li, Z., Wang, N., Zhan, H., Shen, C., Cheng, M. -M., & Reid, I. (n.d.). Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video.

SA State Government through its Research Consortia Program under the Premier’s Research and Industry Fund

2018-2022, Unlocking complex resources through lean processing, $8.6m

Australian Research Council (ARC) Grants:

2020-2022 DP, "Deep learning that scales", $390k
2020-2023, "ARC industrial transformation training centre for integrated operations for complex resources", $5.7m
2018-2023, "ARC industrial transformation research hub for driving farming productivity and disease prevention", $8.8m
2019 LIEF, "A world-class machine learning facility for Australia", $700k
2016 LIEF, "Computational infrastructure for developing deep machine learning models", $250k
2014-2021 ARC 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:

2017-2019 CRC Project, "Intelligent vision, sensing and data fusion for mining and exploration", $4.6m
CIs: Prof. C. Shen, Prof. Nigel Cook, Dr. Damith Ranasinghe
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)

Industry Grants:

2020 Adobe gift
Sole CI
2017-2019 UA170942 "Research and development of computer vision and deep learning technologies for the development of a novel intelligent ultrasound cardiothoracic assessment tool", Contracting Party: an Australian start-up company, $605,628
CIs: Prof. A. van den Hengel, Prof. C. Shen
2017-2018 UA171194 "Research on deep learning for computational photography", Contracting Party: a leading mobile phone manufacturer, US$70k
Sole CI
2018-2020 UA182751 "Research on basic algorithms in computational photography", Contracting Party: a leading mobile phone manufacturer, $863,038
Sole CI
2018-2019 UA183176 "Scene understanding via multi-task learning", $246,000
Sole CI

Miscellaneous:

2019, "Development of new hydrogels to selectively extract and kill invasive glioblastoma cells from normal brain tissue", Partner Investigator (with Guillermo Gomez et al.)

Grant information on Research Data

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

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Current Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2020	Principal Supervisor	Scalable deep learning for scene understanding	Doctor of Philosophy	Doctorate	Full Time	Mr James Paul Bockman
2020	Principal Supervisor	Text Visual Question Answering	Doctor of Philosophy	Doctorate	Full Time	Mr Xinyu Wang
2020	Principal Supervisor	Total Scene Understanding Using Deep Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Yunzhi Zhuge
2020	Principal Supervisor	High performance deep learning based object detection	Master of Philosophy	Master	Full Time	Mr Bowen Zhang
2020	Principal Supervisor	Deep learning for high-performance instance recognition	Doctor of Philosophy	Doctorate	Full Time	Mr Xinlong Wang
2019	Principal Supervisor	Semantic 3D Scene Reconstruction	Doctor of Philosophy	Doctorate	Full Time	Mr Libo Sun
2019	Co-Supervisor	Computer Vision Geometry Deep Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Jiawang Bian
2018	Principal Supervisor	Towards Real-World Application: Fast High-performance Semantic Segmentation	Doctor of Philosophy	Doctorate	Full Time	Mr Zhi Tian
2018	Principal Supervisor	Semantic Understanding Based on 3D Data	Doctor of Philosophy	Doctorate	Full Time	Mr Wei Yin
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
2018	Principal Supervisor	High Performance Per Pixel Prediction Using Deep Learning	Doctor of Philosophy	Doctorate	Full Time	Ms Yifan Liu
2017	Co-Supervisor	Semantic Image Segmentation and Other Dense Per-Pixel Tasks: Practical Approaches	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	Principal 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

Past Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2018 - 2019	Principal Supervisor	High-performance Object Detection and Tracking using Deep Learning	Master of Philosophy	Master	Full Time	Mr Xinyu Wang
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
2015 - 2019	Principal Supervisor	Context Learning and Weakly Supervised Learning for Semantic Segmentation	Doctor of Philosophy	Doctorate	Full Time	Mr Tong Shen
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
Email: chunhua.shen@adelaide.edu.au
Fax: 8313 4366
Campus: North Terrace
Building: Ingkarni Wardli, floor 5
Org Unit: School of Computer Science

Professor Chunhua Shen

Appointments

Awards and Achievements

Education

Research Interests

Journals

Conference Papers

Current Higher Degree by Research Supervision (University of Adelaide)

Past Higher Degree by Research Supervision (University of Adelaide)

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External Profiles