Selected Publications

Google scholar (79159 citations) , DBLP , arXiv .

Journal: 69

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI): 43

International Journal of Computer Vision (IJCV): 26

1. Masked channel modeling for bootstrapping visual pre-training
   \(\cdot\) Y. Liu, X. Wang, M. Zhu, Y. Cao, T. Huang, C. Shen.
   \(\cdot\) International Journal of Computer Vision (IJCV), 2024.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

2. 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

3. 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

4. Scaling up multi-domain semantic segmentation with sentence embeddings
   \(\cdot\) W. Yin, Y. Liu, C. Shen, B. Sun, A. van den Hengel.
   \(\cdot\) International Journal of Computer Vision (IJCV), 2024.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. Metric3D v2: a versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation
    \(\cdot\) M. Hu, W. Yin, C. Zhang, Z. Cai, X. Long, H. Chen, K. Wang, G. Yu, C. Shen, S. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

28. Self-supervised 3d scene flow estimation and motion prediction using local rigidity prior
    \(\cdot\) R. Li, C. Zhang, Z. Wang, C. Shen, G. Lin.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

51. 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.

52. 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

53. 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

54. 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

55. 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

56. 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

57. 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

58. 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

59. 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

60. 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

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. 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

68. 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

69. 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: 184

Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 93

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

Proc. European Conference on Computer Vision (ECCV): 26

Proc. International Conference on Machine Learning (ICML): 6

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

Proc. Advances in Neural Information Processing Systems (NeurIPS): 21

1. FreeCustom: tuning-free customized image generation for multi-concept composition
   \(\cdot\) G. Ding, C. Zhao, W. Wang, Z. Yang, Z. Liu, H. Chen, C. Shen.
   \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’24), 2024.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

2. DiverGen: improving instance segmentation by learning wider data distribution with more diverse generative data
   \(\cdot\) C. Fan, M. Zhu, H. Chen, Y. Liu, W. Wu, H. Zhang, C. Shen.
   \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’24), 2024.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

3. Traffic scene parsing through the tsp6k dataset
   \(\cdot\) P. Jiang, Y. Yang, Y. Cao, Q. Hou, M. Cheng, C. Shen.
   \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’24), 2024.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

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. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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.

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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.

23. 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.

24. 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.

25. 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

26. 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.

27. 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.

28. 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

29. 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.

30. 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

31. 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

32. 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.

33. 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

34. 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.

35. 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

36. 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

37. 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.

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

51. 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

52. 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

53. 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.

54. 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

55. 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

56. 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

57. 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

58. 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

59. 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

60. 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.

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. 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

68. 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

69. 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

70. 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

71. 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

72. 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

73. 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

74. 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

75. 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

76. 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

77. 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

78. 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

79. 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

80. 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

81. 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.

82. 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

83. 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

84. 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

85. 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

86. 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

87. 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

88. 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

89. 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

90. 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

91. 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

92. 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

93. 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

94. FreeCompose: generic zero-shot image composition with diffusion prior
    \(\cdot\) Z. Chen, W. Wang, Z. Yang, Z. Yuan, H. Chen, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’24), 2024.
    \(\cdot\) bibtexgoogle scholarsemantic scholarproject webpage

95. VisionLLaMA: a unified llama backbone for vision tasks
    \(\cdot\) X. Chu, J. Su, B. Zhang, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’24), 2024.
    \(\cdot\) bibtexgoogle scholarsemantic scholarproject webpage

96. 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

97. 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.

98. 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

99. 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

100. 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

101. 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

102. 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

103. 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

104. 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.

105. 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

106. 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

107. 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

108. 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

109. 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

110. 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

111. 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

112. 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

113. 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

114. 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

115. 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

116. 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

117. Robust tracking with weighted online structured learning
     \(\cdot\) R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel.
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118. LACBoost and FisherBoost: optimally building cascade classifiers
     \(\cdot\) C. Shen, P. Wang, H. Li.
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119. A fast algorithm for creating a compact and discriminative visual codebook
     \(\cdot\) L. Wang, L. Zhou, C. Shen.
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120. 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.
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     1. Oral presentation.

121. 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.
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122. 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.
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123. 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.
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124. 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.
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125. 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.
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126. 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.
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127. 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.
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128. 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.
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129. 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.
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130. 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.
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131. 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.
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     1. Oral presentation.

132. 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.
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133. 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.
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134. 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.
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135. 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.
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136. 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

137. 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.
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138. 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

139. 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

140. 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.
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141. 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.
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142. 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.
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143. 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

144. 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.
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145. 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.
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146. 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.
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147. 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.
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148. 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.
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149. 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.
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150. 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.
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151. 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.
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152. 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.
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153. 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.
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     1. Oral presentation, 45 out of 1200 submissions.

154. 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.
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155. 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.
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     1. Spotlight presentation.

156. 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

157. 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.
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158. On the trajectory regularity of ODE-based diffusion sampling
     \(\cdot\) D. Chen, Z. Zhou, C. Wang, C. Shen, S. Lyu.
     \(\cdot\) Proc. International Conference on Machine Learning (ICML’24), 2024.
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159. Floating anchor diffusion model for multi-motif scaffolding
     \(\cdot\) K. Liu, S. Shen, W. Mao, X. Jiao, Z. Sun, H. Chen, C. Shen.
     \(\cdot\) Proc. International Conference on Machine Learning (ICML’24), 2024.
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160. Generative active learning for long-tailed instance segmentation
     \(\cdot\) M. Zhu, C. Fan, H. Chen, Y. Liu, W. Mao, X. Xu, C. Shen.
     \(\cdot\) Proc. International Conference on Machine Learning (ICML’24), 2024.
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161. 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.
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162. 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.

163. 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.
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     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.

164. A simple image segmentation framework via in-context examples
     \(\cdot\) Y. Liu, C. Jing, H. Li, M. Zhu, H. Chen, X. Wang, C. Shen.
     \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’24), 2024.
     \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

165. Unleashing the potential of the diffusion model in few-shot semantic segmentation
     \(\cdot\) M. Zhu, Y. Liu, Z. Luo, C. Jing, H. Chen, G. Xu, X. Wang, C. Shen.
     \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’24), 2024.
     \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

166. 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

167. 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.
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168. 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.
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169. 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.
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170. 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.
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171. 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.
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172. 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.
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173. 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.
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174. 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.
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175. 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.
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176. 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.
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177. 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.
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178. 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.
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179. 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.
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180. 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.
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     1. Others have implemented our paper.

181. 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.
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182. 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.
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183. 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.
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184. 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.
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