Selected Publications

Google scholar (113269 citations) , DBLP , arXiv .

Journal: 79

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI): 47

International Journal of Computer Vision (IJCV): 32


  1. SAM2TAP: repurposing pre-trained video object segmentation models for point tracking
    \(\cdot\) Z. Zhao, W. Wang, C. Zhao, Z. Fu, Z. Zhu, J. Zhou, H. Chen, T. He, C. Shen.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2026.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholar

  2. Multi-modal primitive retrieval for compositional zero-shot learning
    \(\cdot\) C. Jing, H. Zhang, J. Lu, Y. Liu, H. Chen, X. Zhang, C. Shen.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  3. FreeCustom: training-free multi-concept customization for image and video generation
    \(\cdot\) C. Zhao, G. Ding, W. Wang, Z. Yang, Z. Liu, H. Chen, C. Shen.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  4. 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

  5. Segment anything in context with vision foundation models
    \(\cdot\) Y. Liu, M. Zhu, H. Chen, X. Wang, B. Feng, H. Wang, S. Li, R. Vemulapalli, C. Shen.
    \(\cdot\) International Journal of Computer Vision (IJCV), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  6. PonderV2: pave the way for 3D foundation model with a universal pre-training paradigm
    \(\cdot\) H. Zhu, H. Yang, X. Wu, D. Huang, S. Zhang, X. He, H. Zhao, C. Shen, Y. Qiao, T. He, W. Ouyang.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  7. Leaning dual-stream conditional concepts in compositional zero-shot learning
    \(\cdot\) Q. Wang, L. Liu, C. Jing, P. Wang, Y. Zhang, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  8. 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

  9. Diffusion models are efficient data generators for human mesh recovery
    \(\cdot\) Y. Ge, W. Wang, Y. Chen, F. Wang, L. Yang, H. Chen, C. Shen.
    \(\cdot\) IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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

  39. 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

  40. 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

  41. 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

  42. 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

  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. 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

  45. 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

  46. 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

  47. 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

  48. 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

  49. 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

  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. 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

  52. 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

  53. 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

  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. 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

  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. 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

  58. 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

  59. 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

  60. 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

  61. 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

  62. 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

  63. 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

  64. 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

  65. 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

  66. 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

  67. 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

  68. 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

  69. 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

  70. 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

  71. 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

  72. 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

  73. 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

  74. 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

  75. 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

  76. 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

  77. 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

  78. 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

  79. 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: 226

Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 102

Proc. IEEE International Conference on Computer Vision (ICCV): 40

Proc. European Conference on Computer Vision (ECCV): 30

Proc. International Conference on Machine Learning (ICML): 10

Proc. International Conference on Learning Represenations (ICLR): 17

Proc. Advances in Neural Information Processing Systems (NeurIPS): 27


  1. Exploring spatial intelligence from a generative perspective
    \(\cdot\) M. Zhu, S. Jiang, H. Zheng, Z. Luo, H. Zhong, A. Li, K. Wang, J. Rong, Y. Liu, H. Chen, T. Lin, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  2. Eliciting complex spatial reasoning in MLLMs through wide-baseline matching
    \(\cdot\) H. Zhong, M. Zhu, S. Zeng, A. Li, C. Chen, H. Geng, D. Shi, W. Ye, T. Lin, H. Chen, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  3. Unlocking the power of critical factors for 3D visual geometry estimation
    \(\cdot\) G. Xu, H. Geng, H. Zheng, S. Yin, Y. Sun, H. Chen, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  4. R\(^2\)-seg: training-free OOD medical tumor segmentation via anatomical reasoning and statistical rejection
    \(\cdot\) S. Shen, K. Liu, J. Xie, S. Gao, C. Shen, G. Liu, M. Crispin-Ortuzar, S. Gao.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’26), 2026.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholar

    1. Listed as one of the Best Paper Candidates, 74 out of about 16000 submissions.

  5. Preserving source video realism: high-fidelity face swapping for cinematic quality
    \(\cdot\) Z. Luo, Z. Du, Z. Zhu, H. Zhong, M. Zhu, W. Wang, Y. Xi, C. Jing, H. Chen, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  6. StaMo: unsupervised learning of generalizable robot motion from compact state representation
    \(\cdot\) M. Liu, J. Shu, H. Chen, Z. Li, C. Zhao, J. Yang, S. Gao, H. Chen, C. Shen.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  7. GeoMotion: rethinking motion segmentation via latent 4d geometry
    \(\cdot\) X. He, P. Lin, Y. Cui, D. Guo, C. Shen, X. Zhang.
    \(\cdot\) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  8. PhysRVG: physics-aware unified reinforcement learning for video generative models
    \(\cdot\) Q. Zhang, B. Gong, S. Tan, Z. Zhang, X. Zhu, Y. Shen, Y. Li, K. Yao, C. Shen, C. Zou.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’26), 2026.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholar

  9. Metric-Bench: exploring in-context spatial metric reasoning in VLMs for indoor scenes
    \(\cdot\) Y. Xi, H. Zhang, M. Zhu, H. Zhong, Z. Du, H. Zhao, C. Jing, Y. Yin, B. Qin, Y. Yang, Z. Luo, H. Chen, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’26), 2026.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholar

  10. When distillation breaks motion control: restoring generative trajectories for fast video generators
    \(\cdot\) J. Rong, X. Xie, X. Yu, L. Ou, X. Zhang, C. Shen, D. Gong.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’26), 2026.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholar

  11. MMControl: unified multi-modal control for joint audio-video generation
    \(\cdot\) L. Li, W. Wang, C. Zhao, T. Feng, Z. Zhao, H. Chen, C. Shen.
    \(\cdot\) Proc. European Conference on Computer Vision (ECCV’26), 2026.
    \(\cdot\) linkbibtexgoogle scholarsemantic scholarproject webpage

  12. OmniWorld: a multi-domain and multi-modal dataset for 4d world modeling
    \(\cdot\) Y. Zhou, Y. Wang, J. Zhou, W. Chang, H. Guo, Z. Li, K. Ma, X. Li, Y. Wang, H. Zhu, M. Liu, D. Liu, J. Yang, Z. Fu, J. Chen, C. Shen, J. Pang, K. Zhang, T. He.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  13. TINKER: diffusion's gift to 3d–multi-view consistent editing from sparse inputs without per-scene optimization
    \(\cdot\) C. Zhao, X. Li, T. Feng, Z. Zhao, H. Chen, C. Shen.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  14. \(\pi^3\): permutation-equivariant visual geometry learning
    \(\cdot\) Y. Wang, J. Zhou, H. Zhu, W. Chang, Y. Zhou, Z. Li, J. Chen, J. Pang, C. Shen, T. He.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

    1. China3DV 2026 Top 1 Paper, one out of 154.

  15. Time is a feature: exploiting temporal dynamics in diffusion language models
    \(\cdot\) W. Wang, B. Fang, C. Jing, Y. Shen, Y. Shen, Q. Wang, H. Ouyang, H. Chen, C. Shen.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  16. WinT3R: window-based streaming reconstruction with camera token pool
    \(\cdot\) Z. Li, J. Zhou, Y. Wang, H. Guo, W. Chang, Y. Zhou, H. Zhu, J. Chen, C. Shen, T. He.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  17. Uniform discrete diffusion with metric path for video generation
    \(\cdot\) H. Deng, T. Pan, F. Zhang, Y. Liu, Z. Luo, Y. Cui, W. Wang, C. Shen, S. Shan, Z. Zhang, X. Wang.
    \(\cdot\) Proc. International Conference on Learning Representations (ICLR’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  18. ACTIVE-O3: empowering multimodal large language models with active perception via GRPO
    \(\cdot\) M. Zhu, H. Zhong, C. Zhao, Z. Du, Z. Huang, M. Liu, H. Chen, C. Zou, J. Chen, M. Yang, C. Shen.
    \(\cdot\) Proc. International Conference on Machine Learning (ICML’26), 2026.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  19. GAE: unleashing physical potential of VLM with generalizable action expert
    \(\cdot\) M. Liu, Z. Huang, X. Lin, M. Zhu, C. Zhao, Y. Wang, H. Zhu, H. Chen, C. Shen.
    \(\cdot\) Proc. International Conference on Machine Learning (ICML’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  20. SpeedVFI: one-step diffusion for efficient video frame interpolation
    \(\cdot\) G. Ding, X. Xu, H. Chen, C. Shen.
    \(\cdot\) Proc. International Conference on Machine Learning (ICML’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  21. SelfGuard: self-supervised deviation modeling for multi-modal jailbreak detection
    \(\cdot\) C. Jing, H. Guo, H. Jia, T. Bai, C. Sun, Y. Liu, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  22. OmniJigsaw: enhancing omni-modal reasoning via modality-orchestrated reordering
    \(\cdot\) Y. Jia, M. Zhu, H. Zhong, M. Liu, Y. Xi, H. Chen, B. Qin, Y. Yang, Z. Luo, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  23. NoTVLA: semantics-preserving robot adaptation via narrative action interfaces
    \(\cdot\) Z. Huang, M. Liu, X. Lin, M. Zhu, Y. Lin, C. Zhao, Z. Du, X. Li, H. Zhong, Y. Jia, H. Chen, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  24. Scalable adaptation of 3d geometric foundation models via weak supervision from internet video
    \(\cdot\) Z. Gao, K. Liu, J. Bian, D. Chen, D. Shi, G. Lin, H. Chen, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’26), 2026.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  25. 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

  26. 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

  27. Fine-grained abnormality prompt learning for zero-shot anomaly detection
    \(\cdot\) J. Zhu, Y. Ong, C. Shen, G. Pang.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’25), 2025.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholar

  28. Aether: geometric-aware unified world modeling
    \(\cdot\) H. Zhu, Y. Wang, J. Zhou, W. Chang, Y. Zhou, Z. Li, J. Chen, C. Shen, J. Pang, T. He.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’25), 2025.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  29. Pomato: marrying pointmap matching with temporal motions for dynamic 3D reconstruction
    \(\cdot\) S. Zhang, Y. Ge, J. Tian, G. Xu, H. Chen, C. Lv, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’25), 2025.
    \(\cdot\) arXivbibtexgoogle scholarsemantic scholarproject webpage

  30. Unified open-world segmentation with multi-modal prompts
    \(\cdot\) Y. Liu, Y. Yin, C. Jin, M. Zhu, H. Chen, Y. Xi, B. Feng, H. Wang, S. Li, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’25), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  31. SurfaceSplat: connecting surface reconstruction and gaussian splatting
    \(\cdot\) Z. Gao, J. Bian, G. Lin, H. Chen, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’25), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  32. SMSTracker: tri-path score mask sigma fusion for multi-modal tracking
    \(\cdot\) S. Chan, Z. Li, X. Zhang, W. Li, S. Lu, C. Shen.
    \(\cdot\) Proc. IEEE International Conference on Computer Vision (ICCV’25), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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.

  38. 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

  39. 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

  40. Physics aware neural networks for unsupervised binding energy prediction
    \(\cdot\) K. Liu, H. Chen, C. Shen.
    \(\cdot\) Proc. International Conference on Machine Learning (ICML’25), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  41. Omni-R1: reinforcement learning for omnimodal reasoning via two-system collaboration
    \(\cdot\) H. Zhong, M. Zhu, Z. Du, Z. Huang, C. Zhao, M. Liu, W. Wang, H. Chen, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’25), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

  42. Diception: a generalist diffusion model for visual perceptual tasks
    \(\cdot\) C. Zhao, Y. Sun, M. Liu, H. Zheng, M. Zhu, Z. Zhao, H. Chen, T. He, C. Shen.
    \(\cdot\) Proc. Advances in Neural Information Processing Systems (NeurIPS’25), 2025.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

    1. Spotlight presentation.

  43. 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

  44. 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

  45. 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

  46. 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

  47. 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

  48. 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

  49. 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.

  50. 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

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

  52. 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.
    \(\cdot\) bibtexgoogle scholarsemantic scholar

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

  54. 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

  55. 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

  56. 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

  57. 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

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

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

  60. 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

  61. 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

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

  63. 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

  64. 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

  65. 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

  66. 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.

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

  68. 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

  69. 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

  70. 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

  71. 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

  72. 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

  73. 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

  74. 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

  75. 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

  76. 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

  77. 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.

  78. 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

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

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

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

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

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

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

  85. 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

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

  87. 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

  88. 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.

  89. 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.

  90. 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.

  91. 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

  92. 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

  93. 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

  94. 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

  95. 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

  96. 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

  97. 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

  98. 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

  99. 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

  100. 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.

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

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

  103. 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.

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

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

  106. 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

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

  108. 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

  109. 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.

  110. 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

  111. 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

  112. 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.

  113. 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

  114. 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.

  115. 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

  116. 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

  117. 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.

  118. 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

  119. 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.

  120. 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.

  121. 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

  122. 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

  123. 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

  124. 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

  125. 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

  126. 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

  127. 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.

  128. 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

  129. 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

  130. 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

  131. 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

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

  133. 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

  134. 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

  135. 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

  136. 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

  137. 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

  138. 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

  139. 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

  140. 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

  141. 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

  142. 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

  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. 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

  145. 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

  146. 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

  147. 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

  148. 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

  149. 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

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

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

  152. 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

  153. 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

  154. 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

  155. 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

  156. 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.

  157. 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

  158. 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

  159. 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

  160. 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

  161. 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

  162. 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

  163. 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

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

  165. 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

  166. 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

  167. 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

  168. 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.

  169. 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

  170. 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

  171. 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

  172. 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

  173. 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

  174. 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

  175. 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

  176. 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

  177. 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

  178. 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

  179. 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

  180. 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

  181. 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

  182. 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

  183. 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

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

    1. Others have implemented our paper.

  185. 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

  186. 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

  187. 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

  188. 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

  189. 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

  190. 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

  191. 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

  192. 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

  193. 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

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

  195. 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

  196. 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

  197. 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

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

  199. 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

  200. 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

  201. 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.

  202. 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

  203. 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

  204. 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

  205. 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

  206. 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

  207. 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

  208. 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.

  209. 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

  210. 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

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

  212. 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.

  213. 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

  214. 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

  215. 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

  216. 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

  217. 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

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

  219. 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

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

  221. 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

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

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

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

  225. 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

  226. 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.