Selected Publications

Google scholar (13761 citations) , DBLP , arXiv .

Journal

  1. Adaptive importance learning for improving lightweight image super-resolution network
    cdot L. Zhang, P. Wang, C. Shen, L. Liu, W. Wei, Y. Zhang, A. van den Hengel.
    cdot International Journal of Computer Vision (IJCV), 2019.
    cdot arXivbibtexsearch

  2. Adversarial learning of structure-aware fully convolutional networks for landmark localization
    cdot Y. Chen, C. Shen, H. Chen, X. Wei, L. Liu, J. Yang.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019.
    cdot arXivbibtexsearch

  3. RefineNet: multi-path refinement networks for dense prediction
    cdot G. Lin, F. Liu, A. Milan, C. Shen, I. Reid.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019.
    cdot bibtexsearchproject webpage

    1. Pytorch code is here.

  4. Cluster sparsity field: an internal hyperspectral imagery prior for reconstruction
    cdot L. Zhang, W. Wei, Y. Zhang, C. Shen, A. van den Hengel, Q. Shi.
    cdot International Journal of Computer Vision (IJCV), 2018.
    cdot pdfbibtexsearch

  5. Ordinal constraint binary coding for approximate nearest neighbor search
    cdot H. Liu, R. Ji, J. Wang, C. Shen.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018.
    cdot pdfbibtexsearch

  6. FVQA: fact-based visual question answering
    cdot P. Wang, Q. Wu, C. Shen, A. Dick, A. van den Hengel.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018.
    cdot arXivbibtexsearch

  7. Structured learning of binary codes with column generation for optimizing ranking measures
    cdot G. Lin, F. Liu, C. Shen, J. Wu, H. Shen.
    cdot International Journal of Computer Vision (IJCV), 2017.
    cdot arXivbibtexsearchproject webpage

  8. Exploring context with deep structured models for semantic segmentation
    cdot G. Lin, C. Shen, A. van den Hengel, I. Reid.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.
    cdot arXivbibtexsearch

  9. Cross-convolutional-layer pooling for image recognition
    cdot L. Liu, C. Shen, A. van den Hengel.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.
    cdot arXivlinkbibtexsearch

  10. Compositional model based Fisher vector coding for image classification
    cdot L. Liu, P. Wang, C. Shen, L. Wang, A. van den Hengel, C. Wang, H. Shen.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.
    cdot arXivbibtexsearch

  11. Image captioning and visual question answering based on attributes and external knowledge
    cdot Q. Wu, C. Shen, P. Wang, A. Dick, A. van den Hengel.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.
    cdot arXivbibtexsearch

  12. Mining mid-level visual patterns with deep CNN activations
    cdot Y. Li, L. Liu, C. Shen, A. van den Hengel.
    cdot International Journal of Computer Vision (IJCV), 2016.
    cdot arXivlinkbibtexsearchproject webpage

  13. Efficient semidefinite branch-and-cut for MAP-MRF inference
    cdot P. Wang, C. Shen, A. van den Hengel, P. Torr.
    cdot International Journal of Computer Vision (IJCV), 2016.
    cdot arXivlinkbibtexsearch

  14. Unsupervised feature learning for dense correspondences across scenes
    cdot C. Zhang, C. Shen, T. Shen.
    cdot International Journal of Computer Vision (IJCV), 2016.
    cdot arXivbibtexsearchproject webpage

  15. Online metric-weighted linear representations for robust visual tracking
    cdot X. Li, C. Shen, A. Dick, Z. Zhang, Y. Zhuang.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.
    cdot arXivbibtexsearch

  16. Learning depth from single monocular images using deep convolutional neural fields
    cdot F. Liu, C. Shen, G. Lin, I. Reid.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.
    cdot arXivlinkbibtexsearchproject webpage

  17. A generalized probabilistic framework for compact codebook creation
    cdot L. Liu, L. Wang, C. Shen.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.
    cdot arXivlinkbibtexsearch

  18. Pedestrian detection with spatially pooled features and structured ensemble learning
    cdot S. Paisitkriangkrai, C. Shen, A. van den Hengel.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.
    cdot arXivlinkbibtexsearchproject webpage

  19. Large-scale binary quadratic optimization using semidefinite relaxation and applications
    cdot P. Wang, C. Shen, A. van den Hengel, P. Torr.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.
    cdot arXivlinkbibtexsearch

  20. Extrinsic methods for coding and dictionary learning on Grassmann manifolds
    cdot M. Harandi, R. Hartley, C. Shen, B. Lovell, C. Sanderson.
    cdot International Journal of Computer Vision (IJCV), 2015.
    cdot arXivbibtexsearchproject webpage

  21. Supervised hashing using graph cuts and boosted decision trees
    cdot G. Lin, C. Shen, A. van den Hengel.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015.
    cdot arXivlinkbibtexsearchproject webpage

  22. StructBoost: Boosting methods for predicting structured output variables
    cdot C. Shen, G. Lin, A. van den Hengel.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014.
    cdot arXivlinkpdfbibtexsearch

  23. A hierarchical word-merging algorithm with class separability measure
    cdot L. Wang, L. Zhou, C. Shen, L. Liu, H. Liu.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014.
    cdot pdfbibtexsearch

  24. Training effective node classifiers for cascade classification
    cdot C. Shen, P. Wang, S. Paisitkriangkrai, A. van den Hengel.
    cdot International Journal of Computer Vision (IJCV), 2013.
    cdot arXivlinkbibtexsearch

  25. Incremental learning of 3D-DCT compact representations for robust visual tracking
    cdot X. Li, A. Dick, C. Shen, A. van den Hengel, H. Wang.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013.
    cdot arXivlinkpdfbibtexsearchproject webpage

  26. Positive semidefinite metric learning using boosting-like algorithms
    cdot C. Shen, J. Kim, L. Wang, A. van den Hengel.
    cdot Journal of Machine Learning Research (JMLR), 2012.
    cdot arXivlinkbibtexsearchproject webpage

  27. UBoost: Boosting with the Universum
    cdot C. Shen, P. Wang, F. Shen, H. Wang.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2012.
    cdot pdfbibtexsearch

  28. On the dual formulation of boosting algorithms
    cdot C. Shen, H. Li.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2010.
    cdot arXivlinkbibtexsearch

  29. Adaptive object tracking based on an effective appearance filter
    cdot H. Wang, D. Suter, K. Schindler, C. Shen.
    cdot IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2007.
    cdot linkpdfbibtexsearch

    1. Featured article of September issue 2007.

Conference

  1. Knowledge adaptation for efficient semantic segmentation
    cdot T. He, C. Shen, Z. Tian, D. Gong, C. Sun, Y. Yan.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  2. Visual question answering as reading comprehension
    cdot H. Li, P. Wang, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  3. Fast neural architecture search of compact semantic segmentation models via auxiliary cells
    cdot V. Nekrasov, H. Chen, C. Shen, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  4. Decoders matter for semantic segmentation: data-dependent decoding enables flexible feature aggregation
    cdot Z. Tian, T. He, C. Shen, Y. Yan.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  5. Neighbourhood watch: referring expression comprehension via language-guided graph attention networks
    cdot P. Wang, Q. Wu, J. Cao, C. Shen, L. Gao, A. vanden Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  6. Associatively segmenting instances and semantics in point clouds
    cdot X. Wang, S. Liu, X. Shen, C. Shen, J. Jia.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  7. Attention-guided network for ghost-free high dynamic range imaging
    cdot Q. Yan, D. Gong, Q. Shi, A. van den Hengel, C. Shen, I. Reid, Y. Zhang.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  8. CANet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning
    cdot C. Zhang, G. Lin, F. Liu, R. Yao, C. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  9. Mind your neighbours: image annotation with metadata neighbourhood graph co-attention networks
    cdot J. Zhang, Q. Wu, J. Zhang, C. Shen, J. Lu.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot linkbibtexsearch

  10. Structured binary neural networks for accurate image classification and semantic segmentation
    cdot B. Zhuang, C. Shen, M. Tan, L. Liu, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
    cdot arXivbibtexsearch

  11. Indices matter: learning to index for deep image matting
    cdot H. Lu, Y. Dai, C. Shen, S. Xu.
    cdot Proc. International Conference on Computer Vision (ICCV’19), 2019.
    cdot arXivbibtexsearch

  12. FCOS: fully convolutional one-stage object detection
    cdot Z. Tian, C. Shen, H. Chen, T. He.
    cdot Proc. International Conference on Computer Vision (ICCV’19), 2019.
    cdot arXivbibtexsearchproject webpage

  13. Efficient and accurate arbitrary-shaped text detection with pixel aggregation network
    cdot W. Wang, E. Xie, X. Song, Y. Zang, W. Wang, T. Lu, G. Yu, C. Shen.
    cdot Proc. International Conference on Computer Vision (ICCV’19), 2019.
    cdot bibtexsearch

  14. From open set to closed set: counting objects by spatial divide-and-conquer
    cdot H. Xiong, H. Lu, C. Liu, L. Liu, Z. Cao, C. Shen.
    cdot Proc. International Conference on Computer Vision (ICCV’19), 2019.
    cdot arXivbibtexsearchproject webpage

  15. Enforcing geometric constraints of virtual normal for depth prediction
    cdot W. Yin, Y. Liu, C. Shen, Y. Yan.
    cdot Proc. International Conference on Computer Vision (ICCV’19), 2019.
    cdot arXivbibtexsearchproject webpage

  16. Exploiting temporal consistency for real-time video depth estimation
    cdot H. Zhang, C. Shen, Y. Li, Y. Cao, Y. Liu, Y. Yan.
    cdot Proc. International Conference on Computer Vision (ICCV’19), 2019.
    cdot bibtexsearch

  17. Self-training with progressive augmentation for unsupervised cross-domain person re-identification
    cdot X. Zhang, J. Cao, C. Shen, M. You.
    cdot Proc. International Conference on Computer Vision (ICCV’19), 2019.
    cdot arXivbibtexsearch

  18. FSRNet: end-to-end learning face super-resolution with facial priors
    cdot Y. Chen, Y. Tai, X. Liu, C. Shen, J. Yang.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot arXivbibtexsearchproject webpage

  19. An end-to-end textspotter with explicit alignment and attention
    cdot T. He, Z. Tian, W. Huang, C. Shen, Y. Qiao, C. Sun.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot arXivbibtexsearchproject webpage

  20. Visual question answering with memory-augmented networks
    cdot C. Ma, C. Shen, A. Dick, Q. Wu, P. Wang, A. van den Hengel, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot arXivbibtexsearch

  21. Bootstrapping the performance of webly supervised semantic segmentation
    cdot T. Shen, G. Lin, C. Shen, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot bibtexsearchproject webpage

  22. VITAL: visual tracking via adversarial learning
    cdot Y. Song, C. Ma, X. Wu, L. Gong, L. Bao, W. Zuo, C. Shen, R. Lau, M. Yang.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot arXivbibtexsearchproject webpage

  23. Repulsion loss: detecting pedestrians in a crowd
    cdot X. Wang, T. Xiao, Y. Jiang, S. Shao, J. Sun, C. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot arXivbibtexsearch

    1. Others have implemented our paper: Repulsion loss in SSD and Repulsion loss in RetinaNet.

  24. Are you talking to me? reasoned visual dialog generation through adversarial learning
    cdot Q. Wu, P. Wang, C. Shen, I. Reid, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot arXivbibtexsearch

  25. Monocular relative depth perception with web stereo data supervision
    cdot K. Xian, C. Shen, Z. Cao, H. Lu, Y. Xiao, R. Li, Z. Luo.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot bibtexsearch

  26. Towards effective low-bitwidth convolutional neural networks
    cdot B. Zhuang, C. Shen, M. Tan, L. Liu, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot arXivbibtexsearch

  27. Parallel attention: a unified framework for visual object discovery through dialogs and queries
    cdot B. Zhuang, Q. Wu, C. Shen, I. Reid, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18), 2018.
    cdot arXivbibtexsearch

  28. Learning to predict crisp boundaries
    cdot R. Deng, C. Shen, S. Liu, H. Wang, X. Liu.
    cdot Proc. European Conference on Computer Vision (ECCV’18), 2018.
    cdot arXivbibtexsearch

  29. Asking the difficult questions: goal-oriented visual question generation via intermediate rewards
    cdot J. Zhang, Q. Wu, C. Shen, J. Zhang, J. Lu, A. van den Hengel.
    cdot Proc. European Conference on Computer Vision (ECCV’18), 2018.
    cdot arXivbibtexsearch

  30. Adversarial learning with local coordinate coding
    cdot J. Cao, Y. Guo, Q. Wu, C. Shen, J. Huang, M. Tan.
    cdot Proc. International Conference on Machine Learning (ICML’18), 2018.
    cdot arXivbibtexsearch

  31. From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur
    cdot D. Gong, J. Yang, L. Liu, Y. Zhang, I. Reid, C. Shen, A. van den Hengel, Q. Shi.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17), 2017.
    cdot arXivbibtexsearch

  32. Sequential person recognition in photo albums with a recurrent network
    cdot Y. Li, G. Lin, B. Zhuang, L. Liu, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17), 2017.
    cdot arXivbibtexsearch

  33. RefineNet: multi-path refinement networks for high-resolution semantic segmentation
    cdot G. Lin, A. Milan, C. Shen, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17), 2017.
    cdot arXivbibtexsearchproject webpage

    1. Light-weight RefineNet with Pytorch code.

  34. Multi-attention network for one shot learning
    cdot P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, H. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17), 2017.
    cdot pdfbibtexsearch

  35. The VQA-machine: learning how to use existing vision algorithms to answer new questions
    cdot P. Wang, Q. Wu, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17), 2017.
    cdot arXivbibtexsearch

  36. Attend in groups: a weakly-supervised deep learning framework for learning from web data
    cdot B. Zhuang, L. Liu, Y. Li, C. Shen, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17), 2017.
    cdot arXivbibtexsearch

  37. Adversarial PoseNet: a structure-aware convolutional network for human pose estimation
    cdot Y. Chen, C. Shen, X. Wei, L. Liu, J. Yang.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’17), 2017.
    cdot arXivbibtexsearch

  38. Towards end-to-end text spotting with convolutional recurrent neural networks
    cdot H. Li, P. Wang, C. Shen.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’17), 2017.
    cdot arXivbibtexsearch

  39. Semi-global weighted least squares in image filtering
    cdot W. Liu, X. Chen, C. Shen, Z. Liu, J. Yang.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’17), 2017.
    cdot arXivbibtexsearch

  40. When unsupervised domain adaptation meets tensor representations
    cdot H. Lu, L. Zhang, Z. Cao, W. Wei, K. Xian, C. Shen, A. van den Hengel.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’17), 2017.
    cdot bibtexsearch

  41. Towards context-aware interaction recognition
    cdot B. Zhuang, L. Liu, C. Shen, I. Reid.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’17), 2017.
    cdot arXivbibtexsearch

  42. Efficient piecewise training of deep structured models for semantic segmentation
    cdot G. Lin, C. Shen, A. van dan Hengel, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), 2016.
    cdot arXivbibtexsearch

  43. Less is more: zero-shot learning from online textual documents with noise suppression
    cdot R. Qiao, L. Liu, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), 2016.
    cdot arXivbibtexsearch

  44. What's wrong with that object? identifying irregular object from images by modelling the detection score distribution
    cdot P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, H. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), 2016.
    cdot arXivbibtexsearch

  45. What value do explicit high level concepts have in vision to language problems
    cdot Q. Wu, C. Shen, L. Liu, A. Dick, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), 2016.
    cdot arXivbibtexsearch

  46. Ask me anything: free-form visual question answering based on knowledge from external sources
    cdot Q. Wu, P. Wang, C. Shen, A. Dick, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), 2016.
    cdot arXivbibtexsearch

  47. Fast training of triplet-based deep binary embedding networks
    cdot B. Zhuang, G. Lin, C. Shen, I. Reid.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), 2016.
    cdot arXivbibtexsearchproject webpage

  48. Image co-localization by mimicking a good detector's confidence score distribution
    cdot Y. Li, L. Liu, C. Shen, A. van den Hengel.
    cdot Proc. European Conference on Computer Vision (ECCV’16), 2016.
    cdot arXivbibtexsearch

  49. Cluster sparsity field for hyperspectral imagery denoising
    cdot L. Zhang, W. Wei, Y. Zhang, C. Shen, A. van den Hengel, Q. Shi.
    cdot Proc. European Conference on Computer Vision (ECCV’16), 2016.
    cdot bibtexsearch

  50. Image restoration using very deep fully convolutional encoder-decoder networks with symmetric skip connections
    cdot X. Mao, C. Shen, Y. Yang.
    cdot Proc. Advances in Neural Information Processing Systems (NeurIPS’16), 2016.
    cdot arXivlinkbibtexsearchproject webpage

    1. Others have implemented our paper.

  51. Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs
    cdot B. Li, C. Shen, Y. Dai, A. van den Hengel, M. He.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), 2015.
    cdot pdfbibtexsearch

  52. Mid-level deep pattern mining
    cdot Y. Li, L. Liu, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), 2015.
    cdot arXivbibtexsearchproject webpage

  53. Deep convolutional neural fields for depth estimation from a single image
    cdot F. Liu, C. Shen, G. Lin.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), 2015.
    cdot arXivbibtexsearchproject webpage

  54. The treasure beneath convolutional layers: cross convolutional layer pooling for image classification
    cdot L. Liu, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), 2015.
    cdot arXivbibtexsearch

  55. Learning to rank in person re-identification with metric ensembles
    cdot S. Paisitkriangkrai, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), 2015.
    cdot arXivbibtexsearch

  56. Supervised discrete hashing
    cdot F. Shen, C. Shen, W. Liu, H. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), 2015.
    cdot pdfbibtexsearchproject webpage

  57. Learning graph structure for multi-label image classification via clique generation
    cdot M. Tan, Q. Shi, A. van den Hengel, C. Shen, J. Gao, F. Hu, Z. Zhang.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), 2015.
    cdot pdfbibtexsearch

  58. Efficient SDP inference for fully-connected CRFs based on low-rank decomposition
    cdot P. Wang, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), 2015.
    cdot arXivbibtexsearch

  59. Hyperspectral compressive sensing using manifold-structured sparsity prior
    cdot L. Zhang, W. Wei, Y. Zhang, F. Li, C. Shen, Q. Shi.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’15), 2015.
    cdot pdfbibtexsearch

  60. Deeply learning the messages in message passing inference
    cdot G. Lin, C. Shen, I. Reid, A. van den Hengel.
    cdot Proc. Advances in Neural Information Processing Systems (NeurIPS’15), 2015.
    cdot arXivpdfbibtexsearch

  61. Fast supervised hashing with decision trees for high-dimensional data
    cdot G. Lin, C. Shen, Q. Shi, A. van den Hengel, D. Suter.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14), 2014.
    cdot arXivlinkbibtexsearchproject webpage

  62. Optimizing ranking measures for compact binary code learning
    cdot G. Lin, C. Shen, J. Wu.
    cdot Proc. European Conference on Computer Vision (ECCV’14), 2014.
    cdot arXivbibtexsearchproject webpage

  63. Strengthening the effectiveness of pedestrian detection with spatially pooled features
    cdot S. Paisitkriangkrai, C. Shen, A. van den Hengel.
    cdot Proc. European Conference on Computer Vision (ECCV’14), 2014.
    cdot arXivbibtexsearchproject webpage

  64. Encoding high dimensional local features by sparse coding based Fisher vectors
    cdot L. Liu, C. Shen, L. Wang, A. van den Hengel, C. Wang.
    cdot Proc. Advances in Neural Information Processing Systems (NeurIPS’14), 2014.
    cdot arXivbibtexsearch

  65. Learning compact binary codes for visual tracking
    cdot X. Li, C. Shen, A. Dick, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13), 2013.
    cdot linkpdfbibtexsearch

  66. Inductive hashing on manifolds
    cdot F. Shen, C. Shen, Q. Shi, A. van den Hengel, Z. Tang.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13), 2013.
    cdot arXivbibtexsearchproject webpage

  67. A fast semidefinite approach to solving binary quadratic problems
    cdot P. Wang, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13), 2013.
    cdot arXivbibtexsearchproject webpage

    1. Oral presentation, 60 out of 1870 submissions.

  68. Bilinear programming for human activity recognition with unknown MRF graphs
    cdot Z. Wang, Q. Shi, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13), 2013.
    cdot linkpdfbibtexsearch

  69. Part-based visual tracking with online latent structural learning
    cdot R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13), 2013.
    cdot linkpdfbibtexsearchproject webpage

  70. Contextual hypergraph modeling for salient object detection
    cdot X. Li, Y. Li, C. Shen, A. Dick, A. van den Hengel.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’13), 2013.
    cdot arXivbibtexsearchproject webpage

  71. A general two-step approach to learning-based hashing
    cdot G. Lin, C. Shen, D. Suter, A. van den Hengel.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’13), 2013.
    cdot arXivbibtexsearchproject webpage

  72. Efficient pedestrian detection by directly optimizing the partial area under the ROC curve
    cdot S. Paisitkriangkrai, C. Shen, A. van den Hengel.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’13), 2013.
    cdot arXivpdfbibtexsearch

  73. Dictionary learning and sparse coding on Grassmann manifolds: an extrinsic solution
    cdot M. Harandi, C. Sanderson, C. Shen, B. Lovell.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’13), 2013.
    cdot arXivbibtexsearchproject webpage

  74. Learning hash functions using column generation
    cdot X. Li, G. Lin, C. Shen, A. van den Hengel, A. Dick.
    cdot Proc. International Conference on Machine Learning (ICML’13), 2013.
    cdot arXivpdfbibtexsearchproject webpage

    1. Oral presentation.

  75. Non-sparse linear representations for visual tracking with online reservoir metric learning
    cdot X. Li, C. Shen, Q. Shi, A. Dick, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’12), 2012.
    cdot arXivpdfbibtexsearch

  76. Sharing features in multi-class boosting via group sparsity
    cdot S. Paisitkriangkrai, C. Shen, A. van den Hengel.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’12), 2012.
    cdot pdfbibtexsearch

  77. Robust tracking with weighted online structured learning
    cdot R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel.
    cdot Proc. European Conference on Computer Vision (ECCV’12), 2012.
    cdot pdfbibtexsearch

  78. Is margin preserved after random projection?
    cdot Q. Shi, C. Shen, R. Hill, A. van den Hengel.
    cdot Proc. International Conference on Machine Learning (ICML’12), 2012.
    cdot arXivlinkbibtexsearch

    1. This work provides an analysis of margin distortion under random projections, the conditions under which margins are preserved, and presents bounds on the margin distortion.

  79. Real-time visual tracking using compressive sensing
    cdot H. Li, C. Shen, Q. Shi.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’11), 2011.
    cdot pdfbibtexsearch

  80. A generalized probabilistic framework for compact codebook creation
    cdot L. Liu, L. Wang, C. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’11), 2011.
    cdot pdfbibtexsearch

  81. A direct formulation for totally-corrective multi-class boosting
    cdot C. Shen, Z. Hao.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’11), 2011.
    cdot pdfbibtexsearch

  82. A scalable dual approach to semidefinite metric learning
    cdot C. Shen, J. Kim, L. Wang.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’11), 2011.
    cdot pdfbibtexsearch

  83. Is face recognition really a compressive sensing problem?
    cdot Q. Shi, A. Eriksson, A. van den Hengel, C. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’11), 2011.
    cdot pdfbibtexsearch

  84. Graph mode-based contextual kernels for robust SVM tracking
    cdot X. Li, A. Dick, H. Wang, C. Shen, A. van den Hengel.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’11), 2011.
    cdot pdfbibtexsearch

  85. Rapid face recognition using hashing
    cdot Q. Shi, H. Li, C. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’10), 2010.
    cdot pdfbibtexsearch

  86. LACBoost and FisherBoost: optimally building cascade classifiers
    cdot C. Shen, P. Wang, H. Li.
    cdot Proc. European Conference on Computer Vision (ECCV’10), 2010.
    cdot arXivlinkbibtexsearch

  87. Efficiently training a better visual detector with sparse eigenvectors
    cdot S. Paisitkriangkrai, C. Shen, J. Zhang.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09), 2009.
    cdot arXivlinkbibtexsearch

  88. Positive semidefinite metric learning with boosting
    cdot C. Shen, J. Kim, L. Wang, A. van den Hengel.
    cdot Proc. Advances in Neural Information Processing Systems (NeurIPS’09), 2009.
    cdot arXivpdfbibtexsearchproject webpage

  89. A fast algorithm for creating a compact and discriminative visual codebook
    cdot L. Wang, L. Zhou, C. Shen.
    cdot Proc. European Conference on Computer Vision (ECCV’08), 2008.
    cdot linkpdfbibtexsearch

  90. PSDBoost: matrix-generation linear programming for positive semidefinite matrices learning
    cdot C. Shen, A. Welsh, L. Wang.
    cdot Proc. Advances in Neural Information Processing Systems (NeurIPS’08), 2008.
    cdot pdfbibtexsearch

  91. Kernel-based tracking from a probabilistic viewpoint
    cdot Q. Nguyen, A. Robles-Kelly, C. Shen.
    cdot Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07), 2007.
    cdot linkpdfbibtexsearch

  92. Fast global kernel density mode seeking with application to localisation and tracking
    cdot C. Shen, M. Brooks, A. van den Hengel.
    cdot Proc. IEEE International Conference on Computer Vision (ICCV’05), 2005.
    cdot linkpdfbibtexsearch

    1. Oral presentation, 45 out of 1200 submissions.