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Selected journal papers

pdf year title venue authors
’17 Structured learning of binary codes with column generation for optimizing ranking measures [...more] IJCV G. Lin, F. Liu, C. Shen, J. Wu, H. Shen
’17 Compositional model based fisher vector coding for image classification TPAMI L. Liu, P. Wang, C. Shen, L. Wang, A. van den Hengel, C. Wang, H. Shen
’17 Cross-convolutional-layer pooling for image recognition TPAMI L. Liu, C. Shen, A. van den Hengel
’16 Mining mid-level visual patterns with deep CNN activations [...more] IJCV Y. Li, L. Liu, C. Shen, A. van den Hengel
’16 Efficient semidefinite branch-and-cut for MAP-MRF inference IJCV P. Wang, C. Shen, A. van den Hengel, P. Torr
’16 Unsupervised feature learning for dense correspondences across scenes [...more] IJCV C. Zhang, C. Shen, T. Shen
’16 Large-scale binary quadratic optimization using semidefinite relaxation and applications TPAMI P. Wang, C. Shen, A. van den Hengel, P. Torr
’16 Learning depth from single monocular images using deep convolutional neural fields [...more] TPAMI F. Liu, C. Shen, G. Lin, I. Reid
’16 Online metric-weighted linear representations for robust visual tracking TPAMI X. Li, C. Shen, A. Dick, Z. Zhang, Y. Zhuang
’16 Pedestrian detection with spatially pooled features and structured ensemble learning [...more] TPAMI S. Paisitkriangkrai, C. Shen, A. van den Hengel
’16 A generalized probabilistic framework for compact codebook creation TPAMI L. Liu, L. Wang, C. Shen
’15 Extrinsic methods for coding and dictionary learning on Grassmann manifolds [...more] IJCV M. Harandi, R. Hartley, C. Shen, B. Lovell, C. Sanderson
’15 Supervised hashing using graph cuts and boosted decision trees [...more] TPAMI G. Lin, C. Shen, A. van den Hengel
’14 StructBoost: Boosting methods for predicting structured output variables TPAMI C. Shen, G. Lin, A. van den Hengel
’14 A hierarchical word-merging algorithm with class separability measure TPAMI L. Wang, L. Zhou, C. Shen, L. Liu, H. Liu
’13 Training effective node classifiers for cascade classification [...more] IJCV C. Shen, P. Wang, S. Paisitkriangkrai, A. van den Hengel
’13 Incremental learning of 3D-DCT compact representations for robust visual tracking [...more] TPAMI X. Li, A. Dick, C. Shen, A. van den Hengel, H. Wang
’12 Positive semidefinite metric learning using boosting-like algorithms [...more] JMLR C. Shen, J. Kim, L. Wang, A. van den Hengel
’12 UBoost: Boosting with the Universum TPAMI C. Shen, P. Wang, F. Shen, H. Wang
’10 On the dual formulation of boosting algorithms TPAMI C. Shen, H. Li
’07 Adaptive object tracking based on an effective appearance filter TPAMI H. Wang, D. Suter, K. Schindler, C. Shen

Selected conference papers

pdf year title venue authors
’17 The VQA-machine: learning how to use existing vision algorithms to answer new questions CVPR P. Wang, Q. Wu, C. Shen, A. van den Hengel
’17 RefineNet: multi-path refinement networks for high-resolution semantic segmentation [...more] CVPR G. Lin, A. Milan, C. Shen, I. Reid
’17 Sequential person recognition in photo albums with a recurrent network CVPR Y. Li, G. Lin, B. Zhuang, L. Liu, C. Shen, A. van den Hengel
’17 From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur CVPR D. Gong, J. Yang, L. Liu, Y. Zhang, I. Reid, C. Shen, A. van den Hengel, Q. Shi
’17 Attend in groups: a weakly-supervised deep learning framework for learning from web data CVPR B. Zhuang, L. Liu, C. Shen, I. Reid
’17 Multi-attention network for one shot learning CVPR P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, H. Shen
’16 Ask me anything: free-form visual question answering based on knowledge from external sources CVPR Q. Wu, P. Wang, C. Shen, A. Dick, A. van den Hengel
’16 What value do explicit high level concepts have in vision to language problems CVPR Q. Wu, C. Shen, L. Liu, A. Dick, A. van den Hengel
’16 What's wrong with that object? identifying irregular object from images by modelling the detection score distribution CVPR P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, H. Shen
’16 Efficient piecewise training of deep structured models for semantic segmentation CVPR G. Lin, C. Shen, A. van dan Hengel, I. Reid
’16 Fast training of triplet-based deep binary embedding networks [...more] CVPR B. Zhuang, G. Lin, C. Shen, I. Reid
’16 Less is more: zero-shot learning from online textual documents with noise suppression CVPR R. Qiao, L. Liu, C. Shen, A. van den Hengel
’16 Cluster sparsity field for hyperspectral imagery denoising ECCV L. Zhang, W. Wei, Y. Zhang, C. Shen, A. van den Hengel, Q. Shi
’16 Image co-localization by mimicking a good detector's confidence score distribution ECCV Y. Li, L. Liu, C. Shen, A. van den Hengel
’16 Image restoration using very deep fully convolutional encoder-decoder networks with symmetric skip connections [...more] NIPS X. Mao, C. Shen, Y. Yang
’15 Mid-level deep pattern mining [...more] CVPR Y. Li, L. Liu, C. Shen, A. van den Hengel
’15 Deep convolutional neural fields for depth estimation from a single image [...more] CVPR F. Liu, C. Shen, G. Lin
’15 Supervised discrete hashing [...more] CVPR F. Shen, C. Shen, W. Liu, H. Shen
’15 The treasure beneath convolutional layers: cross convolutional layer pooling for image classification CVPR L. Liu, C. Shen, A. van den Hengel
’15 Efficient SDP inference for fully-connected CRFs based on low-rank decomposition CVPR P. Wang, C. Shen, A. van den Hengel
’15 Learning to rank in person re-identification with metric ensembles CVPR S. Paisitkriangkrai, C. Shen, A. van den Hengel
’15 Learning graph structure for multi-label image classification via clique generation CVPR M. Tan, Q. Shi, A. van den Hengel, C. Shen, J. Gao, F. Hu, Z. Zhang
’15 Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs CVPR B. Li, C. Shen, Y. Dai, A. van den Hengel, M. He
’15 Hyperspectral compressive sensing using manifold-structured sparsity prior ICCV L. Zhang, W. Wei, Y. Zhang, F. Li, C. Shen, Q. Shi
’15 Deeply learning the messages in message passing inference NIPS G. Lin, C. Shen, I. Reid, A. van den Hengel
’14 Fast supervised hashing with decision trees for high-dimensional data [...more] CVPR G. Lin, C. Shen, Q. Shi, A. van den Hengel, D. Suter
’14 Optimizing ranking measures for compact binary code learning [...more] ECCV G. Lin, C. Shen, J. Wu
’14 Strengthening the effectiveness of pedestrian detection with spatially pooled features [...more] ECCV S. Paisitkriangkrai, C. Shen, A. van den Hengel
’14 Encoding high dimensional local features by sparse coding based Fisher vectors NIPS L. Liu, C. Shen, L. Wang, A. van den Hengel, C. Wang
’13 Inductive hashing on manifolds [...more] CVPR F. Shen, C. Shen, Q. Shi, A. van den Hengel, Z. Tang
’13 Learning compact binary codes for visual tracking CVPR X. Li, C. Shen, A. Dick, A. van den Hengel
’13 Bilinear programming for human activity recognition with unknown MRF graphs CVPR Z. Wang, Q. Shi, C. Shen, A. van den Hengel
’13 A fast semidefinite approach to solving binary quadratic problems [...more] CVPR P. Wang, C. Shen, A. van den Hengel
’13 Part-based visual tracking with online latent structural learning [...more] CVPR R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel
’13 A general two-step approach to learning-based hashing [...more] ICCV G. Lin, C. Shen, D. Suter, A. van den Hengel
’13 Efficient pedestrian detection by directly optimizing the partial area under the ROC curve ICCV S. Paisitkriangkrai, C. Shen, A. van den Hengel
’13 Contextual hypergraph modeling for salient object detection [...more] ICCV X. Li, Y. Li, C. Shen, A. Dick, A. van den Hengel
’13 Dictionary learning and sparse coding on Grassmann manifolds: an extrinsic solution [...more] ICCV M. Harandi, C. Sanderson, C. Shen, B. Lovell
’13 Learning hash functions using column generation [...more] ICML X. Li, G. Lin, C. Shen, A. van den Hengel, A. Dick
’12 Non-sparse linear representations for visual tracking with online reservoir metric learning CVPR X. Li, C. Shen, Q. Shi, A. Dick, A. van den Hengel
’12 Sharing features in multi-class boosting via group sparsity CVPR S. Paisitkriangkrai, C. Shen, A. van den Hengel
’12 Robust tracking with weighted online structured learning ECCV R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel
’12 Is margin preserved after random projection? ICML Q. Shi, C. Shen, R. Hill, A. van den Hengel
’11 A direct formulation for totally-corrective multi-class boosting CVPR C. Shen, Z. Hao
’11 A generalized probabilistic framework for compact codebook creation CVPR L. Liu, L. Wang, C. Shen
’11 Is face recognition really a compressive sensing problem? CVPR Q. Shi, A. Eriksson, A. van den Hengel, C. Shen
’11 Real-time visual tracking using compressive sensing CVPR H. Li, C. Shen, Q. Shi
’11 A scalable dual approach to semidefinite metric learning CVPR C. Shen, J. Kim, L. Wang
’11 Graph mode-based contextual kernels for robust SVM tracking ICCV X. Li, A. Dick, H. Wang, C. Shen, A. van den Hengel
’10 Rapid face recognition using hashing CVPR Q. Shi, H. Li, C. Shen
’10 LACBoost and FisherBoost: optimally building cascade classifiers ECCV C. Shen, P. Wang, H. Li
’09 Efficiently training a better visual detector with sparse eigenvectors CVPR S. Paisitkriangkrai, C. Shen, J. Zhang
’09 Positive semidefinite metric learning with boosting [...more] NIPS C. Shen, J. Kim, L. Wang, A. van den Hengel
’08 A fast algorithm for creating a compact and discriminative visual codebook ECCV L. Wang, L. Zhou, C. Shen
’08 PSDBoost: matrix-generation linear programming for positive semidefinite matrices learning NIPS C. Shen, A. Welsh, L. Wang
’07 Kernel-based tracking from a probabilistic viewpoint CVPR Q. Nguyen, A. Robles-Kelly, C. Shen
’05 Fast global kernel density mode seeking with application to localisation and tracking ICCV C. Shen, M. Brooks, A. van den Hengel