Selected Publications

Google scholar (69440 citations) , DBLP , arXiv .

Journal: 67

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI): 41

International Journal of Computer Vision (IJCV): 24

Journal of Machine Learning Research (JMLR): 1

ACM Transactions on Graphics (TOG): 1


  1. End-to-end video text spotting with Transformer
    \(\cdot\) W. Wu, C. Shen, Y. Cai, D. Zhang, Y. Fu, P. Luo, H. Zhou.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2024.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  2. Towards robust monocular depth estimation: a new baseline and benchmark
    \(\cdot\) K. Xian, Z. Cao, C. Shen, G. Lin.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2024.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  3. Super vision transformer
    \(\cdot\) M. Lin, M. Chen, Y. Zhang, C. Shen, R. Ji, L. Cao.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  4. A dynamic feature interaction framework for multi-task visual perception
    \(\cdot\) Y. Xi, H. Chen, N. Wang, P. Wang, Y. Zhang, C. Shen, Y. Liu.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2023.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  5. From open set to closed set: supervised spatial divide-and-conquer for object counting
    \(\cdot\) H. Xiong, H. Lu, C. Liu, L. Liu, C. Shen, Z. Cao.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  6. SPL-Net: spatial-semantic patch learning network for facial attribute recognition with limited labeled data
    \(\cdot\) Y. Yan, Y. Shu, S. Chen, J. Xue, C. Shen, H. Wang.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2023.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  7. SegViT v2: exploring efficient and continual semantic segmentation with plain vision transformers
    \(\cdot\) B. Zhang, L. Liu, M. Phan, Z. Tian, C. Shen, Y. Liu.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  8. Single-path bit sharing for automatic loss-aware model compression
    \(\cdot\) J. Liu, B. Zhuang, P. Chen, C. Shen, J. Cai, M. Tan.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  9. SPTS v2: single-point scene text spotting
    \(\cdot\) Y. Liu, J. Zhang, D. Peng, M. Huang, X. Wang, J. Tang, C. Huang, D. Lin, C. Shen, X. Bai, L. Jin.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  10. SC-DepthV3: robust self-supervised monocular depth estimation for dynamic scenes
    \(\cdot\) L. Sun, J. Bian, H. Zhan, W. Yin, I. Reid, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  11. Learning from partially labeled data for multi-organ and tumor segmentation
    \(\cdot\) Y. Xie, J. Zhang, Y. Xia, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  12. Structured binary neural networks for image recognition
    \(\cdot\) B. Zhuang, C. Shen, M. Tan, P. Chen, L. Liu, I. Reid.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  13. Dynamic convolution for 3D point cloud instance segmentation
    \(\cdot\) T. He, C. Shen, A. van den Hengel.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  14. FCOS: a simple and strong anchor-free object detector
    \(\cdot\) Z. Tian, C. Shen, H. Chen, T. He.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholarproject webpage

  15. Instance and panoptic segmentation using conditional convolutions
    \(\cdot\) Z. Tian, B. Zhang, H. Chen, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  16. Towards accurate reconstruction of 3D scene shape from a single monocular image
    \(\cdot\) W. Yin, J. Zhang, O. Wang, S. Niklaus, S. Chen, Y. Liu, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  17. DeepEMD: differentiable earth mover's distance for few-shot learning
    \(\cdot\) C. Zhang, Y. Cai, G. Lin, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  18. Unsupervised scale-consistent depth learning from video
    \(\cdot\) J. Bian, H. Zhan, N. Wang, Z. Li, L. Zhang, C. Shen, M. Cheng, I. Reid.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  19. Joint classification and regression for visual tracking with fully convolutional Siamese networks
    \(\cdot\) Y. Cui, D. Guo, Y. Shao, Z. Wang, C. Shen, L. Zhang, S. Chen.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  20. Exploring the capacity of an orderless box discretization network for multi-orientation scene text detection
    \(\cdot\) Y. Liu, T. He, H. Chen, X. Wang, C. Luo, S. Zhang, C. Shen, L. Jin.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  21. NAS-FCOS: efficient search for object detection architectures
    \(\cdot\) N. Wang, Y. Gao, H. Chen, P. Wang, Z. Tian, C. Shen, Y. Zhang.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholarproject webpage

  22. A dual-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.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholarproject webpage

  23. BiSeNet v2: bilateral network with guided aggregation for real-time semantic segmentation
    \(\cdot\) C. Yu, C. Gao, J. Wang, G. Yu, C. Shen, N. Sang.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  24. Memory-efficient hierarchical neural architecture search for image restoration
    \(\cdot\) H. Zhang, Y. Li, H. Chen, C. Gong, Z. Bai, C. Shen.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  25. Auto-rectify network for unsupervised indoor depth estimation
    \(\cdot\) J. Bian, H. Zhan, N. Wang, T. Chin, C. Shen, I. Reid.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  26. ABCNet v2: adaptive bezier-curve network for real-time end-to-end text spotting
    \(\cdot\) Y. Liu, C. Shen, L. Jin, T. He, P. Chen, C. Liu, H. Chen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  27. Towards end-to-end text spotting in natural scenes
    \(\cdot\) P. Wang, H. Li, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  28. PAN++: towards efficient and accurate end-to-end spotting of arbitrarily-shaped text
    \(\cdot\) W. Wang, E. Xie, X. Li, X. Liu, D. Liang, Z. Yang, T. Lu, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  29. SOLO: a simple framework for instance segmentation
    \(\cdot\) X. Wang, R. Zhang, C. Shen, T. Kong, L. Li.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  30. Virtual normal: enforcing geometric constraints for accurate and robust depth prediction
    \(\cdot\) W. Yin, Y. Liu, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  31. Effective training of convolutional neural networks with low-bitwidth weights and activations
    \(\cdot\) B. Zhuang, J. Liu, M. Tan, L. Liu, I. Reid, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  32. Separating content from style using adversarial learning for recognizing text in the wild
    \(\cdot\) C. Luo, Q. Lin, Y. Liu, L. Jin, C. Shen.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  33. Real-time image smoothing via iterative least squares
    \(\cdot\) W. Liu, P. Zhang, X. Huang, J. Yang, C. Shen, I. Reid.
    \(\cdot\) ACM Transactions on Graphics (TOG), 2020.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  34. Improving generative adversarial networks with local coordinate coding
    \(\cdot\) J. Cao, Y. Guo, Q. Wu, C. Shen, J. Huang, M. Tan.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  35. 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), 2020.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  36. Structured knowledge distillation for dense prediction
    \(\cdot\) Y. Liu, C. Shun, J. Wang, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  37. Index networks
    \(\cdot\) H. Lu, Y. Dai, C. Shen, S. Xu.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  38. Ordered or orderless: a revisit for video based person re-identification
    \(\cdot\) L. Zhang, Z. Shi, J. Zhou, M. Cheng, Y. Liu, J. Bian, Z. Zeng, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  39. Plenty is plague: fine-grained learning for visual question answering
    \(\cdot\) Y. Zhou, R. Ji, J. Su, X. Sun, D. Meng, Y. Gao, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholar

  40. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  41. 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.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholarproject webpage

    1. Pytorch code is here.

  42. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  43. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  44. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  45. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  46. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  47. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  48. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  49. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  50. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  51. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  52. Unsupervised feature learning for dense correspondences across scenes
    \(\cdot\) C. Zhang, C. Shen, T. Shen.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2016.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  53. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  54. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  55. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  56. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  57. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  58. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  59. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  60. 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.
    \(\cdot\) arXivlinkpdfbibtexgoogle scholarsemantic scholar

  61. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  62. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  63. 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.
    \(\cdot\) arXivlinkpdfbibtexgoogle scholarsemantic scholarproject webpage

  64. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  65. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  66. On the dual formulation of boosting algorithms
    \(\cdot\) C. Shen, H. Li.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2010.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  67. 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.
    \(\cdot\) linkpdfbibtexgoogle scholarsemantic scholar

    1. Featured article of September issue 2007.

Conference: 174

Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 90

Proc. IEEE International Conference on Computer Vision (ICCV): 34

Proc. European Conference on Computer Vision (ECCV): 24

Proc. International Conference on Machine Learning (ICML): 3

Proc. International Conference on Learning Represenations (ICLR): 4

Proc. Advances in Neural Information Processing Systems (NeurIPS): 19


  1. Matcher: segment anything with one shot using all-purpose feature matching
    \(\cdot\) Y. Liu, M. Zhu, H. Li, H. Chen, X. Wang, C. Shen.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’24), 2024.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  2. De novo protein design using geometric vector field networks
    \(\cdot\) W. Mao, M. Zhu, Z. Sun, S. Shen, L. Wu, H. Chen, C. Shen.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’24), 2024.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

    1. Spotlight presentation.

  3. Object-aware inversion and reassembly for image editing
    \(\cdot\) Z. Yang, D. Gui, W. Wang, H. Chen, B. Zhuang, C. Shen.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’24), 2024.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  4. Learning conditional attributes for compositional zero-shot learning
    \(\cdot\) Q. Wang, L. Liu, C. Jing, H. Chen, G. Liang, P. Wang, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’23), 2023.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  5. Images speak in images: a generalist painter for in-context visual learning
    \(\cdot\) X. Wang, W. Wang, Y. Cao, C. Shen, T. Huang.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’23), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  6. Zolly: zoom focal length correctly for perspective-distorted human mesh reconstruction
    \(\cdot\) W. Wang, Y. Ge, H. Mei, Z. Cai, Q. Sun, C. Shen, Y. Wang, L. Yang, T. Komura.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

    1. Oral presentation.

  7. SegGPT: towards segmenting everything in context
    \(\cdot\) X. Wang, X. Zhang, Y. Cao, W. Wang, C. Shen, T. Huang.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  8. Diffumask: synthesizing images with pixel-level annotations for semantic segmentation using diffusion models
    \(\cdot\) W. Wu, Y. Zhao, M. Shou, H. Zhou, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  9. Pose-free 3d scene reconstruction with frozen depth models
    \(\cdot\) G. Xu, W. Yin, H. Chen, C. Shen, K. Cheng, F. Zhao.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  10. Metric3D: towards zero-shot metric 3d prediction from a single image
    \(\cdot\) W. Yin, C. Zhang, H. Chen, Z. Cai, G. Yu, K. Wang, X. Chen, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  11. CTVIS: consistent training for online video instance segmentation
    \(\cdot\) K. Ying, Q. Zhong, W. Mao, Z. Wang, H. Chen, L. Wu, Y. Liu, C. Fan, Y. Zhuge, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  12. Robust geometry-preserving depth estimation using differentiable rendering
    \(\cdot\) C. Zhang, W. Yin, G. Yu, Z. Wang, T. Chen, B. Fu, J. Zhou, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  13. Generative prompt model for weakly supervised object localization
    \(\cdot\) Y. Zhao, Q. Ye, W. Wu, C. Shen, F. Wan.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  14. Segprompt: boosting open-world segmentation via category-level prompt learning
    \(\cdot\) M. Zhu, H. Li, H. Chen, C. Fan, W. Mao, C. Jing, Y. Liu, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’23), 2023.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  15. Conditional positional encodings for vision transformers
    \(\cdot\) X. Chu, Z. Tian, B. Zhang, X. Wang, X. Wei, H. Xia, C. Shen.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’23), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  16. DatasetDM: synthesizing data with perception annotations using diffusion models
    \(\cdot\) W. Wu, Y. Zhao, H. Chen, Y. Gu, R. Zhao, Y. He, H. Zhou, M. Shou, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’23), 2023.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  17. Boosting robustness of image matting with context assembling and strong data augmentation
    \(\cdot\) Y. Dai, B. Price, H. Zhang, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  18. Catching both gray and black swans: open-set supervised anomaly detection
    \(\cdot\) C. Ding, G. Pan, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  19. RigidFlow: self-supervised scene flow learning on point clouds by local rigidity prior
    \(\cdot\) R. Li, C. Zhang, G. Lin, Z. Wang, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  20. Retrieval augmented classification for long-tail visual recognition
    \(\cdot\) A. Long, W. Yin, T. Ajanthan, V. Nguyen, P. Purkait, R. Garg, A. Blair, C. Shen, A. van den Hengel.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  21. FreeSOLO: learning to segment objects without annotations
    \(\cdot\) X. Wang, Z. Yu, S. De Mello, J. Kautz, A. Anandkumar, C. Shen, J. Alvarez.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  22. TopFormer: token pyramid transformer for mobile semantic segmentation
    \(\cdot\) W. Zhang, Z. Huang, G. Yu, T. Chen, G. Luo, X. Wang, W. Liu, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  23. Efficient decoder-free object detection with transformers
    \(\cdot\) P. Chen, M. Zhang, Y. Shen, K. Sheng, Y. Gao, X. Sun, K. Li, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’22), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  24. DisCo: remedying self-supervised learning on lightweight models with distilled contrastive learning
    \(\cdot\) Y. Gao, J. Zhuang, S. Lin, H. Cheng, X. Sun, K. Li, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation.

  25. PointInst3D: segmenting 3D instances by points
    \(\cdot\) T. He, W. Yin, C. Shen, A. van den Hengel.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’22), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  26. Poseur: direct human pose regression with transformers
    \(\cdot\) W. Mao, Y. Ge, C. Shen, Z. Tian, X. Wang, Z. Wang, A. van den Hengel.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’22), 2022.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  27. PyramidCLIP: hierarchical feature alignment for vision-language model pretraining
    \(\cdot\) Y. Gao, J. Liu, Z. Xu, J. Zhang, K. Li, R. Ji, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  28. Adv-attribute: inconspicuous and transferable adversarial attack on face recognition
    \(\cdot\) S. Jia, B. Yin, T. Yao, S. Ding, C. Shen, X. Yang, C. Ma.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  29. Multi-dataset training of transformers for robust action recognition
    \(\cdot\) J. Liang, E. Zhang, J. Zhang, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  30. Text-adaptive multiple visual prototype matching for video-text retrieval
    \(\cdot\) C. Lin, A. Wu, J. Liang, J. Zhang, W. Ge, W. Zheng, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  31. Fully convolutional one-stage 3D object detection on LiDAR range images
    \(\cdot\) Z. Tian, X. Chu, X. Wang, X. Wei, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  32. SegViT: semantic segmentation with plain vision transformers
    \(\cdot\) B. Zhang, Z. Tian, Q. Tang, X. Chu, X. Wei, C. Shen, Y. Liu.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  33. Hierarchical normalization for robust monocular depth estimation
    \(\cdot\) C. Zhang, W. Yin, Z. Wang, G. Yu, B. Fu, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  34. DENSE: data-free one-shot federated learning
    \(\cdot\) J. Zhang, C. Chen, B. Li, L. Lyu, S. Wu, S. Ding, C. Shen, C. Wu.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’22), 2022.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  35. AQD: towards accurate quantized object detection
    \(\cdot\) P. Chen, J. Liu, B. Zhuang, M. Tan, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

    1. Oral presentation.

  36. Learning affinity-aware upsampling for deep image matting
    \(\cdot\) Y. Dai, H. Lu, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  37. Graph attention tracking
    \(\cdot\) D. Guo, Y. Shao, Y. Cui, Z. Wang, L. Zhang, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  38. DyCo3D: robust instance segmentation of 3d point clouds through dynamic convolution
    \(\cdot\) T. He, C. Shen, A. van den Hengel.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  39. HCRF-Flow: scene flow from point clouds with continuous high-order CRFs and position-aware flow embedding
    \(\cdot\) R. Li, G. Lin, T. He, F. Liu, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  40. Generic perceptual loss for modelling structured output dependencies
    \(\cdot\) Y. Liu, W. Yin, Y. Chen, H. Chen, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  41. FCPose: fully convolutional multi-person pose estimation with dynamic instance-aware convolutions
    \(\cdot\) W. Mao, Z. Tian, X. Wang, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholarproject webpage

  42. Feature decomposition and reconstruction learning for effective facial expression recognition
    \(\cdot\) D. Ruan, Y. Yan, S. Lai, Z. Chai, C. Shen, H. Wang.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  43. Learning spatial-semantic relationship for facial attribute recognition with limited labeled data
    \(\cdot\) Y. Shu, Y. Yan, S. Chen, J. Xue, C. Shen, H. Wang.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  44. BoxInst: high-performance instance segmentation with box annotations
    \(\cdot\) Z. Tian, C. Shen, X. Wang, H. Chen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  45. Dense contrastive learning for self-supervised visual pre-training
    \(\cdot\) X. Wang, R. Zhang, C. Shen, T. Kong, L. Li.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation.

  46. End-to-end video instance segmentation with Transformers
    \(\cdot\) Y. Wang, Z. Xu, X. Wang, C. Shen, B. Cheng, H. Shen, H. Xia.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

    1. Oral presentation.

  47. Learning to recover 3D scene shape from a single image
    \(\cdot\) W. Yin, J. Zhang, O. Wang, S. Niklaus, L. Mai, S. Chen, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Listed as one of the Best Paper Candidates, 32 out of about 6000 submissions.

  48. DoDNet: learning to segment multi-organ and tumors from multiple partially labeled datasets
    \(\cdot\) J. Zhang, Y. Xie, Y. Xia, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  49. FATNN: fast and accurate ternary neural networks
    \(\cdot\) P. Chen, B. Zhuang, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  50. Channel-wise knowledge distillation for dense prediction
    \(\cdot\) C. Shu, Y. Liu, J. Gao, L. Xu, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  51. Occluded person re-identification with single-scale global representations
    \(\cdot\) C. Yan, G. Pang, J. Jiao, X. Bai, X. Feng, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

    1. Oral presentation.

  52. BV-Person: a large-scale dataset for bird-view person re-identification
    \(\cdot\) C. Yan, G. Pang, L. Wang, J. Jiao, X. Feng, C. Shen, J. Li.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  53. A simple baseline for semi-supervised semantic segmentation with strong data augmentation
    \(\cdot\) J. Yuan, Y. Liu, C. Shen, Z. Wang, H. Li.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  54. Meta navigator: search for a good adaptation policy for few-shot learning
    \(\cdot\) C. Zhang, H. Ding, G. Lin, R. Li, C. Wang, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  55. Twins: revisiting the design of spatial attention in vision transformers
    \(\cdot\) X. Chu, Z. Tian, Y. Wang, B. Zhang, H. Ren, X. Wei, H. Xia, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  56. Dynamic neural representational decoders for high-resolution semantic segmentation
    \(\cdot\) B. Zhang, Y. Liu, Z. Tian, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’21), 2021.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  57. BlendMask: top-down meets bottom-up for instance segmentation
    \(\cdot\) H. Chen, K. Sun, Z. Tian, C. Shen, Y. Huang, Y. Yan.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation.

  58. ABCNet: arbitrarily-shaped scene text spotting with adaptive Bezier-curve network in real time
    \(\cdot\) Y. Liu, H. Chen, C. Shen, T. He, L. Jin, L. Wang.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation.

  59. Self-trained deep ordinal regression for end-to-end video anomaly detection
    \(\cdot\) G. Pang, C. Yan, C. Shen, A. van den Hengel, X. Bai.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  60. REVERIE: remote embodied visual referring expression in real indoor environments
    \(\cdot\) Y. Qi, Q. Wu, P. Anderson, X. Wang, W. Wang, C. Shen, A. van den Hengel.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

    1. Oral presentation.

  61. NAS-FCOS: fast neural architecture search for object detection
    \(\cdot\) N. Wang, Y. Gao, H. Chen, P. Wang, Z. Tian, C. Shen, Y. Zhang.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  62. On the general value of evidence, and bilingual scene-text visual question answering
    \(\cdot\) X. Wang, Y. Liu, C. Shen, C. Ng, C. Luo, L. Jin, C. Chan, A. van den Hengel, L. Wang.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  63. PolarMask: single shot instance segmentation with polar representation
    \(\cdot\) E. Xie, P. Sun, X. Song, W. Wang, X. Liu, D. Liang, C. Shen, P. Luo.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation.

  64. Context prior for scene segmentation
    \(\cdot\) C. Yu, J. Wang, C. Gao, G. Yu, C. Shen, N. Sang.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  65. DeepEMD: few-shot image classification with differentiable earth mover's distance and structured classifiers
    \(\cdot\) C. Zhang, Y. Cai, G. Lin, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation.

  66. Memory-efficient hierarchical neural architecture search for image denoising
    \(\cdot\) H. Zhang, Y. Li, H. Chen, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  67. Mask encoding for single shot instance segmentation
    \(\cdot\) R. Zhang, Z. Tian, C. Shen, M. You, Y. Yan.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  68. Training quantized neural networks with a full-precision auxiliary module
    \(\cdot\) B. Zhuang, L. Liu, M. Tan, C. Shen, I. Reid.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

    1. Oral presentation.

  69. Learning and memorizing representative prototypes for 3D point cloud semantic and instance segmentation
    \(\cdot\) T. He, D. Gong, Z. Tian, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  70. Instance-aware embedding for point cloud instance segmentation
    \(\cdot\) T. He, Y. Liu, C. Shen, X. Wang, C. Sun.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  71. Weighing counts: sequential crowd counting by reinforcement learning
    \(\cdot\) L. Liu, H. Lu, H. Zou, H. Xiong, Z. Cao, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  72. Efficient semantic video segmentation with per-frame inference
    \(\cdot\) Y. Liu, C. Shen, C. Yu, J. Wang.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  73. Conditional convolutions for instance segmentation
    \(\cdot\) Z. Tian, C. Shen, H. Chen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation.

  74. Soft expert reward learning for vision-and-language navigation
    \(\cdot\) H. Wang, Q. Wu, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  75. AE TextSpotter: learning visual and linguistic representation for ambiguous text spotting
    \(\cdot\) W. Wang, X. Liu, X. Ji, E. Xie, D. Liang, Z. Yang, T. Lu, C. Shen, P. Luo.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  76. Scene text image super-resolution in the wild
    \(\cdot\) W. Wang, E. Xie, X. Liu, W. Wang, D. Liang, C. Shen, X. Bai.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  77. SOLO: segmenting objects by locations
    \(\cdot\) X. Wang, T. Kong, C. Shen, Y. Jiang, L. Li.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  78. Segmenting transparent objects in the wild
    \(\cdot\) E. Xie, W. Wang, W. Wang, M. Ding, C. Shen, P. Luo.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  79. Representative graph neural network
    \(\cdot\) C. Yu, Y. Liu, C. Gao, C. Shen, N. Sang.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  80. SOLOv2: dynamic and fast instance segmentation
    \(\cdot\) X. Wang, R. Zhang, T. Kong, L. Li, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’20), 2020.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  81. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  82. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  83. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  84. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  85. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  86. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  87. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  88. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  89. 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.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholar

  90. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

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

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

  93. 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. IEEE International Conference on Computer Vision (ICCV’19), 2019.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  94. 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. IEEE International Conference on Computer Vision (ICCV’19), 2019.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

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

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

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

  98. Unsupervised scale-consistent depth and ego-motion learning from monocular video
    \(\cdot\) J. Bian, Z. Li, N. Wang, H. Zhan, C. Shen, M. Cheng, I. Reid.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’19), 2019.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  99. Multi-marginal wasserstein GAN
    \(\cdot\) J. Cao, L. Mo, Y. Zhang, K. Jia, C. Shen, M. Tan.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’19), 2019.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  100. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  101. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  102. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  103. 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.
    \(\cdot\) bibtexgoogle scholarsemantic scholarproject webpage

  104. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  105. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

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

  106. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  107. 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.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  108. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  109. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  110. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  111. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  112. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  113. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  114. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  115. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Light-weight RefineNet with Pytorch code.

  116. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  117. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  118. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  119. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  120. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  121. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  122. 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.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  123. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  124. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  125. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  126. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  127. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  128. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  129. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  130. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  131. 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.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  132. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

    1. Others have implemented our paper.

  133. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  134. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  135. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  136. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  137. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  138. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholarproject webpage

  139. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  140. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  141. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  142. 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.
    \(\cdot\) arXivpdfbibtexgoogle scholarsemantic scholar

  143. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholarproject webpage

  144. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  145. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  146. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  147. 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.
    \(\cdot\) linkpdfbibtexgoogle scholarsemantic scholar

  148. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  149. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation, 60 out of 1870 submissions.

  150. 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.
    \(\cdot\) linkpdfbibtexgoogle scholarsemantic scholar

  151. 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.
    \(\cdot\) linkpdfbibtexgoogle scholarsemantic scholarproject webpage

  152. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  153. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  154. 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.
    \(\cdot\) arXivpdfbibtexgoogle scholarsemantic scholar

  155. 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.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  156. 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.
    \(\cdot\) arXivpdfbibtexgoogle scholarsemantic scholarproject webpage

    1. Oral presentation.

  157. 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.
    \(\cdot\) arXivpdfbibtexgoogle scholarsemantic scholar

  158. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  159. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  160. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

    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.

  161. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  162. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  163. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  164. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  165. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  166. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  167. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

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

  169. 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.
    \(\cdot\) arXivlinkbibtexgoogle scholarsemantic scholar

  170. 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.
    \(\cdot\) arXivpdfbibtexgoogle scholarsemantic scholarproject webpage

  171. 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.
    \(\cdot\) linkpdfbibtexgoogle scholarsemantic scholar

  172. 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.
    \(\cdot\) pdfbibtexgoogle scholarsemantic scholar

  173. 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.
    \(\cdot\) linkpdfbibtexgoogle scholarsemantic scholar

  174. 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.
    \(\cdot\) linkpdfbibtexgoogle scholarsemantic scholar

    1. Oral presentation, 45 out of 1200 submissions.