Selected Publications

Google scholar (86081 citations) , DBLP , arXiv .

Journal: 72

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI): 44

International Journal of Computer Vision (IJCV): 28

1. Paragraph-to-image generation with information-enriched diffusion model
   \(\cdot\) W. Wu, Z. Li, Y. He, M. Shou, C. Shen, L. Cheng, Y. Li, T. Gao, Z. Di.
   \(\cdot\) International Journal of Computer Vision (IJCV), 2025.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

2. 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

3. AutoStory: generating diverse storytelling images with minimal human effort
   \(\cdot\) W. Wang, C. Zhao, H. Chen, Z. Chen, K. Zheng, C. Shen.
   \(\cdot\) International Journal of Computer Vision (IJCV), 2024.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

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. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. VimTS: a unified video and image text spotter for enhancing the cross-domain generalization
    \(\cdot\) Y. Liu, M. Huang, H. Yan, L. Deng, W. Wu, H. Lu, C. Shen, L. Jin, X. Bai.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

51. 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

52. 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

53. 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

54. 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.

55. 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

56. 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

57. 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

58. 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

59. 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

60. 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

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. 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

68. 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

69. 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

70. 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

71. 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

72. 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: 193

Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 95

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): 11

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

1. MovieBench: a hierarchical movie level dataset for long video generation
   \(\cdot\) W. Wu, M. Liu, Z. Zhu, F. Haoen, X. Xia, W. Wang, K. Lin, C. Shen, M. Shou.
   \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’25), 2025.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

2. SegAgent: exploring pixel understanding capabilities in MLLMs by imitating human annotator trajectories
   \(\cdot\) M. Zhu, Y. Tian, H. Chen, C. Zhou, Q. Guo, Y. Liu, M. Yang, C. Shen.
   \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’25), 2025.
   \(\cdot\) bibtexgoogle scholarsemantic scholar

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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.

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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.

25. 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.

26. 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.

27. 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

28. 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.

29. 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.

30. 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

31. 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.

32. 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

33. 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

34. 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.

35. 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

36. 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.

37. 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

38. 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

39. 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.

40. 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

41. 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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

51. 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

52. 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

53. 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

54. 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

55. 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.

56. 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

57. 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

58. 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

59. 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

60. 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

61. 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

62. 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.

63. 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

64. 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

65. 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

66. 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

67. 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

68. 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

69. 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

70. 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

71. 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

72. 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

73. 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

74. 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

75. 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

76. 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

77. 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

78. 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

79. 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

80. 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

81. 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

82. 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

83. 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.

84. 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

85. 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

86. 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

87. 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

88. 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

89. 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

90. 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

91. 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

92. 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

93. 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

94. 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

95. 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

96. 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

97. 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

98. 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

99. 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.

100. 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

101. 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

102. 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

103. 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

104. 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

105. 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

106. 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.

107. 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

108. 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

109. 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

110. 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

111. 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

112. 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

113. 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.
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114. 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.
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115. 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.
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116. 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.
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117. 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

118. 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.
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119. 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.
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120. LACBoost and FisherBoost: optimally building cascade classifiers
     \(\cdot\) C. Shen, P. Wang, H. Li.
     \(\cdot\) Proc. European Conference on Computer Vision (ECCV’10), 2010.
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121. 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.
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122. 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.

123. 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

124. 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

125. 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

126. 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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127. 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

128. 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

129. 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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130. 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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131. 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

132. 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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133. 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.

134. 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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135. 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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136. 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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137. 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

138. 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

139. 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

140. 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

141. 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

142. 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

143. 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

144. 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

145. 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

146. 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

147. 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

148. 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

149. 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

150. 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

151. 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

152. 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

153. 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

154. 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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155. 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.

156. Revisiting convolution architecture in the realm of DNA foundation models
     \(\cdot\) Y. Bo, W. Mao, Y. Shao, W. Bai, P. Ye, X. Ma, J. Zhao, H. Chen, C. Shen.
     \(\cdot\) Proc. International Conference on Learning Representations (ICLR’25), 2025.
     \(\cdot\) bibtexgoogle scholarsemantic scholar

157. PerturboLLaVA: reducing multimodal hallucinations with perturbative visual training
     \(\cdot\) C. Chen, M. Liu, C. Jing, Y. Zhou, F. Rao, H. Chen, B. Zhang, C. Shen.
     \(\cdot\) Proc. International Conference on Learning Representations (ICLR’25), 2025.
     \(\cdot\) bibtexgoogle scholarsemantic scholar

158. Boltzmann-aligned inverse folding model as a predictor of mutational effects on protein-protein interactions
     \(\cdot\) X. Jiao, W. Mao, W. Jin, P. Yang, H. Chen, C. Shen.
     \(\cdot\) Proc. International Conference on Learning Representations (ICLR’25), 2025.
     \(\cdot\) bibtexgoogle scholarsemantic scholar

     1. Spotlight presentation.

159. Framer: interactive frame interpolation
     \(\cdot\) W. Wang, Q. Wang, K. Zheng, H. Ouyang, Z. Chen, B. Gong, H. Chen, Y. Shen, C. Shen.
     \(\cdot\) Proc. International Conference on Learning Representations (ICLR’25), 2025.
     \(\cdot\) bibtexgoogle scholarsemantic scholar

160. What matters when repurposing diffusion models for general dense perception tasks?
     \(\cdot\) G. Xu, Y. Ge, M. Liu, C. Fan, K. Xie, Z. Zhao, H. Chen, C. Shen.
     \(\cdot\) Proc. International Conference on Learning Representations (ICLR’25), 2025.
     \(\cdot\) bibtexgoogle scholarsemantic scholar

161. Depth any video with scalable synthetic data
     \(\cdot\) H. Yang, D. Huang, W. Yin, C. Shen, H. Liu, X. He, B. Lin, W. Ouyang, T. Hi.
     \(\cdot\) Proc. International Conference on Learning Representations (ICLR’25), 2025.
     \(\cdot\) bibtexgoogle scholarsemantic scholar

162. MovieDreamer: hierarchical generation for coherent long visual sequences
     \(\cdot\) C. Zhao, M. Liu, W. Wang, W. Chen, F. Wang, H. Chen, B. Zhang, C. Shen.
     \(\cdot\) Proc. International Conference on Learning Representations (ICLR’25), 2025.
     \(\cdot\) bibtexgoogle scholarsemantic scholar

163. 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

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

     1. Spotlight presentation.

165. 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

166. 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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167. 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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168. 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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169. 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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170. 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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171. 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.

172. 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.

173. 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

174. 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

175. 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

176. 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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177. 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

178. 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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179. 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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180. 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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181. 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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182. 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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183. 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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184. 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

185. 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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186. 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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187. 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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188. 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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189. 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.

190. 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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191. 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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192. 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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193. 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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