Low-Level Vision

Let's enhance! Our team has been working on various image/video restoration and enhancement problems such as super-resolution, denoising, low-light enhancement etc. Some notable methods developed by us include SRCNN, ESRGAN, EDVR, BasicVSR, and Zero-DCE.

Super-Resolution

  • BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
    K. C. K. Chan, S. Zhou, X. Xu, C. C. Loy
    Technical report, arXiv:2104.13371, 2021
    [arXiv] [Project Page]
  • GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
    K. C. K. Chan, X. Wang, X. Xu, J. Gu, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR, Oral)
    [PDF] [Supplementary Material] [arXiv] [Project Page]
  • BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
    K. C. K. Chan, X. Wang, K. Yu, C. Dong, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page]
  • Robust Reference-based Super-Resolution via C2-Matching
    Y. Jiang, K. C. K. Chan, X. Wang, C. C. Loy, Z. Liu
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page]
  • Understanding Deformable Alignment in Video Super-Resolution
    K. C. K. Chan, X. Wang, K. Yu, C. Dong, C. C. Loy
    in Proceedings of AAAI Conference on Artificial Intelligence, 2021 (AAAI)
    [arXiv] [Project Page]
  • Cross-Scale Internal Graph Neural Network for Image Super-Resolution
    S. Zhou, J. Zhang, W. Zuo, C. C. Loy
    in Proceedings of Neural Information Processing Systems, 2020 (NeurIPS)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
    X. Pan, X. Zhan, B. Dai, D. Lin, C. C. Loy, P. Luo
    European Conference on Computer Vision, 2020 (ECCV, Oral)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Non-Local Recurrent Network for Image Restoration
    D. Liu, B. Wen, Y. Fan, C. C. Loy, T. S. Huang
    in Proceedings of Neural Information Processing Systems, 2018 (NeurIPS)
    [PDF] [arXiv] [Project Page]
  • ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
    X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, C. C. Loy, Y. Qiao, X. Tang
    in Workshop Proceedings of European Conference on Computer Vision, 2018 (ECCVW)
    [PDF] [arXiv] [Project Page]
  • Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
    X. Wang, K. Yu, C. Dong, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018 (CVPR)
    [PDF] [arXiv] [Project Page]
  • Deep Cascaded Bi-Network for Face Hallucination
    S. Zhu, S. Liu, C. C. Loy, X. Tang
    in Proceedings of European Conference on Computer Vision, 2016 (ECCV)
    [PDF] [arXiv] [Project Page]
  • Accelerating the Super-Resolution Convolutional Neural Network
    C. Dong, C. C. Loy, X. Tang
    in Proceedings of European Conference on Computer Vision, 2016 (ECCV)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Depth Map Super Resolution by Deep Multi-Scale Guidance
    T.-W. Hui, C. C. Loy, X. Tang
    in Proceedings of European Conference on Computer Vision, 2016 (ECCV)
    [PDF] [arXiv]
  • Image Super-Resolution Using Deep Convolutional Networks
    C. Dong, C. C. Loy, K. He, X. Tang
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 295-307, 2015 (TPAMI)
    [DOI] [arXiv] [Supplementary Material] [Project Page]
  • Learning a Deep Convolutional Network for Image Super-Resolution
    C. Dong, C. C. Loy, K. He, X. Tang
    in Proceedings of European Conference on Computer Vision, pp 184-199, 2014 (ECCV)
    [PDF] [Supplementary Material] [Project Page]
  • Boosting Optical Character Recognition: A Super-Resolution Approach
    C. Dong, X. Zhu, Y. Deng, C. C. Loy, Y. Qiao
    Technical report, arXiv:1506.02211, 2015
    [arXiv]

Restoration | Enhancement

  • ReconfigISP: Reconfigurable Camera Image Processing Pipeline
    K. Yu, Z. Li, Y. Peng, C. C. Loy, J. Gu
    in Proceedings of IEEE/CVF International Conference on Computer Vision, 2021 (ICCV)
    [Coming Soon]
  • Path-Restore: Learning Network Path Selection for Image Restoration
    K. Yu, X. Wang, C. Dong, X. Tang, C. C. Loy
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 (TPAMI)
    [DOI] [arXiv] [Project Page]
  • Low-Light Image and Video Enhancement Using Deep Learning: A Survey
    C. Li, C. Guo, L. Han, J. Jiang, M. Cheng, J. Gu, C. C. Loy
    Technical report, arXiv:2104.10729, 2021
    [arXiv] [Project Page]
  • Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
    C. Li, C. Guo, C. C. Loy
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 (TPAMI)
    [DOI] [arXiv] [Project Page]
  • Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network
    R. Feng, C. Li, H. Chen, S. Li, C. C. Loy, J. Gu
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page]
  • Deep Animation Video Interpolation in the Wild
    S-Y. Li, S. Zhao, W. Yu, W. Sun, D. Metaxas, C. C. Loy, Z. Liu
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page]
  • Flexible Piecewise Curves Estimation for Photo Enhancement
    C. Li, C. Guo, Q. Ai, S. Zhou, C. C. Loy
    Technical report, arXiv:2010.13412, 2020
    [arXiv]
  • Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
    C. Guo, C. Li, J. Guo, C. C. Loy, J. Hou, S. Kwong, R. Cong
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
    X. Wang, C. K. Chan, K. Yu, C. Dong, X. Tang, C. C. Loy
    in Workshop Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, NTIRE, 2019 (CVPRW)
    [PDF] [arXiv] [Project Page]
  • Deep Network Interpolation for Continuous Imagery Effect Transition
    X. Wang, K. Yu, C. Dong, X. Tang, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR)
    [PDF] [arXiv] [Project Page]
  • Aesthetic-Driven Image Enhancement by Adversarial Learning
    Y. Deng, C. C. Loy, X. Tang
    ACM Multimedia, 2018 (ACM MM)
    [PDF] [arXiv] [Project Page]
  • Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning
    K. Yu, C. Dong, L. Lin, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018 (CVPR, Spotlight)
    [PDF] [arXiv] [Project Page]
  • Image Aesthetic Assessment: An Experimental Survey
    Y. Deng, C. C. Loy, X. Tang
    IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 80–106, 2017 (SPM)
    [DOI] [arXiv]
  • Deep Specialized Network for Illuminant Estimation
    W. Shi, C. C. Loy, X. Tang
    in Proceedings of European Conference on Computer Vision, 2016 (ECCV)
    [PDF] [Project Page]
  • Compression Artifacts Reduction by a Deep Convolutional Network
    C. Dong, Y. Deng, C. C. Loy, X. Tang
    in Proceedings of International Conference on Computer Vision, pp. 576-584, 2015 (ICCV)
    [PDF] [arXiv] [Project Page]

Optical Flow Estimation

  • LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation
    T.-W. Hui, C. C. Loy
    European Conference on Computer Vision, 2020 (ECCV)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization
    T.-W. Hui, X. Tang, C. C. Loy
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 (TPAMI)
    [DOI] [arXiv] [Project Page]
  • LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
    T.-W. Hui, X. Tang, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018 (CVPR, Spotlight)
    [PDF] [arXiv] [Project Page] [YouTube]

Editing and Generation

We like algorithms that could generate new visual contents, e.g., face generation, face reenactment, image inpainting, scene de-occlusion, etc.

Face Manipulation and Editing

  • Talk-to-Edit: Fine-Grained Facial Editing via Dialog
    Y. Jiang, Z. Huang, X. Pan, C. C. Loy, Z. Liu
    in Proceedings of IEEE/CVF International Conference on Computer Vision, 2021 (ICCV)
    [Coming Soon]
  • Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation
    H. Zhou, Y. Sun, W. Wu, C. C. Loy, X. Wang, Z. Liu
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page]
  • Pareidolia Face Reenactment
    L. Song, W. Wu, C. Fu, C. Qian, C. C. Loy, R. He
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page] [YouTube]
  • Audio-Driven Emotional Video Portraits
    X. Ji, H. Zhou, K. Wang, W. Wu, X. Cao, C. C. Loy, F. Xu
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page]
  • MEAD: A Large-scale Audio-visual Dataset for Emotional Talking Face Generation
    K. Wang, Q. Wu, L. Song, Z. Yang, W. Wu, C. Qian, R. He, Y. Qiao, C. C. Loy
    European Conference on Computer Vision, 2020 (ECCV)
    [PDF] [Supplementary Material] [Project Page]
  • One-shot Face Reenactment
    Y. Zhang, S. Zhang, Y. He, C. Li, C. C. Loy, Z. Liu
    in Proceedings of British Machine Vision Conference, 2019 (BMVC, Spotlight)
    [PDF] [arXiv] [Project Page]
  • Instance-level Facial Attributes Transfer with Geometry-aware Flow
    W. Yin, Z. Liu, C. C. Loy
    in Proceedings of AAAI Conference on Artificial Intelligence, 2019 (AAAI, Spotlight)
    [PDF] [arXiv] [Project Page]
  • ReenactGAN: Learning to Reenact Faces via Boundary Transfer
    W. Wu, Y. Zhang, C. Li, C. Qian, C. C. Loy
    in Proceedings of European Conference on Computer Vision, 2018 (ECCV)
    [PDF] [arXiv] [Project Page] [YouTube]

Image and Video Generation

  • Focal Frequency Loss for Image Reconstruction and Synthesis
    L. Jiang, B. Dai, W. Wu, C. C. Loy
    in Proceedings of IEEE/CVF International Conference on Computer Vision, 2021 (ICCV)
    [arXiv] [Project Page]
  • Positional Encoding as Spatial Inductive Bias in GANs
    R. Xu, X. Wang, K. Chen, B. Zhou, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page] [YouTube]
  • Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs
    X. Pan, B. Dai, Z. Liu, C. C. Loy, P. Luo
    International Conference on Learning Representations, 2021 (ICLR, Oral)
    [PDF] [arXiv] [Project Page]
  • Texture Memory-Augmented Deep Patch-Based Image Inpainting
    R. Xu, M. Guo, J. Wang, X. Li, B. Zhou, C. C. Loy
    Technical report, arXiv:2009.13240, 2020
    [arXiv]
  • Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
    X. Pan, X. Zhan, B. Dai, D. Lin, C. C. Loy, P. Luo
    European Conference on Computer Vision, 2020 (ECCV, Oral)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • TSIT: A Simple and Versatile Framework for Image-to-Image Translation
    L. Jiang, C. Zhang, M. Huang, C. Liu, J. Shi, C. C. Loy
    European Conference on Computer Vision, 2020 (ECCV, Spotlight)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Everybody’s Talkin’: Let Me Talk as You Want
    L. Song, W. Wu, C. Qian, R. He, C. C. Loy
    Technical report, arXiv:2001.05201, 2020
    [arXiv] [Project Page]
  • Self-Supervised Scene De-occlusion
    X. Zhan, X. Pan, B. Dai, Z. Liu, D. Lin, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR, Oral)
    [PDF] [arXiv] [Supplementary Material] [Project Page] [YouTube]
  • TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting
    Z. Yang, W. Zhu, W. Wu, C. Qian, Q. Zhou, B. Zhou, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Real or Not Real, That is the Question
    Y. Xiangli, Y. Deng, B. Dai, C. C. Loy, D. Lin
    International Conference on Learning Representations, 2020 (ICLR, Spotlight)
    [PDF] [Project Page]
  • High-Quality Video Generation from Static Structural Annotations
    L. Sheng, J. Pan, J. Guo, J. Shao, C. C. Loy
    International Journal of Computer Vision, 2020 (IJCV)
    [DOI]
  • TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation
    W. Wu, K. Cao, C. Li, C. Qian, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Deep Flow-Guided Video Inpainting
    R. Xu, X. Li, B. Zhou, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR)
    [PDF] [arXiv] [Project Page]
  • Dense Intrinsic Appearance Flow for Human Pose Transfer
    Y. Li, C. Huang, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR)
    [PDF] [Supplementary Material] [Project Page]
  • Disentangling Content and Style via Unsupervised Geometry Distillation
    W. Wu, K. Cao, C. Li, C. Qian, C. C. Loy
    International Conference on Learning Representations Workshop, 2019 (ICLRW)
    [PDF]
  • Be Your Own Prada: Fashion Synthesis with Structural Coherence
    S. Zhu, S. Fidler, R. Urtasun, D. Lin, C. C. Loy
    in Proceedings of International Conference on Computer Vision, 2017 (ICCV)
    [PDF] [Project Page]

Image and Video Understanding

We explore effective and efficient methods to detect, segment and recognize objects in complex scenes.

Object Detection

  • CARAFE++: Unified Content-Aware ReAssembly of FEatures
    J. Wang, K. Chen, R. Xu, Z. Liu, C. C. Loy, D. Lin
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 (TPAMI)
    [DOI] [arXiv]
  • Side-Aware Boundary Localization for More Precise Object Detection
    J. Wang, W. Zhang, Y. Cao, K. Chen, J. Pang, T. Gong, J. Shi, C. C. Loy, D. Lin
    European Conference on Computer Vision, 2020 (ECCV)
    [PDF] [arXiv] [Supplementary Material]
  • RGB-D Salient Object Detection with Cross-Modality Modulation and Selection
    C. Li, R. Cong, Y. Piao, Q. Xu, C. C. Loy
    European Conference on Computer Vision, 2020 (ECCV)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Feature Pyramid Grids
    K. Chen, Y. Cao, C. C. Loy, D. Lin, C. Feichtenhofer
    Technical report, arXiv:2004.03580, 2020
    [arXiv]
  • Prime Sample Attention in Object Detection
    Y. Cao, K. Chen, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • CARAFE: Content-Aware ReAssembly of FEatures
    J. Wang, K. Chen, R. Xu, Z. Liu, C. C. Loy, D. Lin
    in Proceedings of International Conference on Computer Vision, 2019 (ICCV, Oral)
    [PDF] [arXiv] [Supplementary Material]
  • Region Proposal by Guided Anchoring
    J. Wang, K. Chen, S. Yang, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR)
    [PDF] [arXiv] [Project Page]
  • MMDetection: OpenMMLab Detection Toolbox and Benchmark
    K. Chen, J. Wang, J. Pang, Y. Cao, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Xu, Z. Zhang, D. Cheng, C. Zhu, T. Cheng, Q. Zhao, B. Li, X. Lu, R. Zhu, Y. Wu, J. Dai, J. Wang, J. Shi, W. Ouyang, C. C. Loy, D. Lin
    Technical report, arXiv:1906.07155, 2019
    [arXiv] [Project Page]
  • Optimizing Video Object Detection via a Scale-Time Lattice
    K. Chen, J. Wang, S. Yang, X. Zhang, Y. Xiong, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018 (CVPR)
    [PDF] [arXiv] [Project Page] [YouTube]
  • Fusing Object Context to Detect Functional Area for Cognitive Robots
    H. Cheng, J. Cai, Q. Liu, Z. Zhang, K. Yang, C. C. Loy, L. Lin
    in Proceedings of IEEE International Conference on Robotics and Automation, 2018 (ICRA)
    [PDF]
  • Discover and Learn New Objects from Documentaries
    K. Chen, H. Song, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017 (CVPR, Spotlight)
    [PDF] [arXiv] [Project Page]
  • DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks
    W. Ouyang, X. Zeng, X. Wang, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, H. Li, K. Wang, J. Yan, C. C. Loy, X. Tang
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 (TPAMI)
    [DOI]
  • DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
    W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2403-2412, 2015 (CVPR)
    [PDF] [Project Page]

Semantic Segmentation

  • K-Net: Towards Unified Image Segmentation
    W. Zhang, J. Pang, K. Chen, C. C. Loy
    Technical report, arXiv:2106.14855, 2021
    [arXiv]
  • FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation
    Y Zang, C. Huang, C. C. Loy
    in Proceedings of IEEE/CVF International Conference on Computer Vision, 2021 (ICCV)
    [arXiv] [Project Page]
  • Seesaw Loss for Long-Tailed Instance Segmentation
    J. Wang, W. Zhang, Y. Zang, Y. Cao, J. Pang, T. Gong, K. Chen, Z. Liu, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv]
  • Hybrid Task Cascade for Instance Segmentation
    K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR)
    [PDF] [arXiv] [Project Page]
  • Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation
    X. Li, C. C. Loy
    in Proceedings of European Conference on Computer Vision, 2018 (ECCV)
    [PDF] [arXiv]
  • PSANet: Point-wise Spatial Attention Network for Scene Parsing
    H. Zhao, Y. Zhang, S. Liu, J. Shi, C. C. Loy, D. Lin, J. Jia
    in Proceedings of European Conference on Computer Vision, 2018 (ECCV)
    [PDF] [Project Page]
  • Mix-and-Match Tuning for Self-supervised Semantic Segmentation
    X. Zhan, Z. Liu, P. Luo, X. Tang, C. C. Loy
    in Proceedings of AAAI Conference on Artificial Intelligence, 2018 (AAAI, Spotlight)
    [PDF] [arXiv] [Project Page]
  • Video Object Segmentation with Re-identification
    X. Li, Y. Qi, Z. Wang. K. Chen, Z. Liu, J. Shi, P. Luo, X. Tang, C. C. Loy
    in Workshop of DAVIS Challenge on Video Object Segmentation, IEEE Conference on Computer Vision and Pattern Recognition, 2017 (CVPRW)
    [PDF] [arXiv] [Project Page]
  • Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade
    X. Li, Z. Liu, P. Luo, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017 (CVPR, Spotlight)
    [PDF] [arXiv] [Project Page]
  • Deep Learning Markov Random Field for Semantic Segmentation
    Z. Liu, X. Li, P. Luo, C. C. Loy, X. Tang
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 (TPAMI)
    [arXiv] [Project Page]
  • Semantic Image Segmentation via Deep Parsing Network
    Z. Liu, X. Li, P. Luo, C. C. Loy, X. Tang
    in Proceedings of International Conference on Computer Vision, pp. 1377-1385, 2015 (ICCV, Oral)
    [PDF] [arXiv] [Project Page]

Tracking and Association

  • MessyTable: Instance Association in Multiple Camera Views
    Z. Cai, J. Zhang, D. Ren, C. Yu, H. Zhao, S. Yi, C. K. Yeo, C. C. Loy
    European Conference on Computer Vision, 2020 (ECCV)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Robust Multi-Modality Multi-Object Tracking
    W. Zhang, H. Zhou, S. Sun, Z. Wang, J. Shi, C. C. Loy
    in Proceedings of International Conference on Computer Vision, 2019 (ICCV)
    [PDF] [arXiv] [Project Page]

Recognition

  • Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition
    G. Yin, L. Sheng, B. Liu, N. Yu, X. Wang, J. Shao, C. C. Loy
    in Proceedings of European Conference on Computer Vision, 2018 (ECCV)
    [PDF] [arXiv]
  • PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
    X. Zhang, Z. Li, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017 (CVPR)
    [PDF] [arXiv] [Project Page]
  • Learning to Disambiguate by Asking Discriminative Questions
    Y. Li, C. Huang, X. Tang, C. C. Loy
    in Proceedings of International Conference on Computer Vision, 2017 (ICCV)
    [PDF] [arXiv] [Project Page]
  • Local Similarity-Aware Deep Feature Embedding
    C. Huang, C. C. Loy, X. Tang
    in Proceedings of Neural Information Processing Systems, 2016 (NIPS)
    [PDF] [arXiv]
  • Unsupervised Learning of Discriminative Attributes and Visual Representations
    C. Huang, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR)
    [PDF] [Project Page]
  • Learning Deep Representation for Imbalanced Classification
    C. Huang, Y. Li, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR, Spotlight)
    [PDF] [Project Page]
  • A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
    L. Yang, P. Luo, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3973-3981, 2015 (CVPR)
    [PDF] [arXiv] [Project Page]
  • Deep Representation Learning with Target Coding
    S. Yang, P. Luo, C. C. Loy, K. W. Shum, X. Tang
    in Proceedings of AAAI Conference on Artificial Intelligence, 2015 (AAAI, Oral, nominated as easily accessible paper)
    [PDF] [Supplementary Material] [Project Page]

3D Scene Understanding and Reconstruction

Our team has been working on various tasks related to 3D reconstruction and perception, e.g, 3D shape generation and 3D human recovery

3D Reconstruction | Completion

  • 3D Human Texture Estimation from a Single Image with Transformers
    X. Xu, C. C. Loy
    in Proceedings of IEEE/CVF International Conference on Computer Vision, 2021 (ICCV, Oral)
    [Coming Soon]
  • Unsupervised 3D Shape Completion through GAN Inversion
    J. Zhang, X. Chen, Z. Cai, L. Pan, H. Zhao, S. Yi, C. K. Yeo, B. Dai, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021 (CVPR)
    [PDF] [Supplementary Material] [arXiv] [Project Page]
  • Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs
    X. Pan, B. Dai, Z. Liu, C. C. Loy, P. Luo
    International Conference on Learning Representations, 2021 (ICLR, Oral)
    [PDF] [arXiv] [Project Page]
  • Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory
    Y. Rong, Z. Liu, C. C. Loy
    Technical report, arXiv:2012.14739, 2020
    [arXiv]
  • Delving Deep into Hybrid Annotations for 3D Human Recovery in the Wild
    Y. Rong, Z. Liu, C. Li, K. Cao, C. C. Loy
    in Proceedings of International Conference on Computer Vision, 2019 (ICCV)
    [PDF] [arXiv] [Project Page] [Supplementary Material]

3D Perception

  • Exploring Data Augmentation for Multi-Modality 3D Object Detection
    W. Zhang, Z. Wang, C. C. Loy
    Technical report, arXiv:2012.12741, 2020
    [arXiv] [Project Page]

Deep Learning

We investigate new deep learning methods that are more efficient, robust, accurate, scalable, transferable, and explainable.

Unsupervised | Self-Supervised Learning

  • Unsupervised Object-Level Representation Learning from Scene Images
    J. Xie, X. Zhan, Z. Liu, Y. S. Ong, C. C. Loy
    Technical report, arXiv:2106.11952, 2021
    [arXiv] [Project Page]
  • Delving into Inter-Image Invariance for Unsupervised Visual Representations
    J. Xie, X. Zhan, Z. Liu, Y. S. Ong, C. C. Loy
    Technical report, arXiv:2008.11702, 2020
    [arXiv] [Project Page]
  • Online Deep Clustering for Unsupervised Representation Learning
    X. Zhan, J. Xie, Z. Liu, Y. S. Ong, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR)
    [PDF] [arXiv] [Project Page]
  • Self-Supervised Learning via Conditional Motion Propagation
    X. Zhan, X. Pan, Z. Liu, D. Lin, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR)
    [PDF] [arXiv] [Project Page]
  • Mix-and-Match Tuning for Self-supervised Semantic Segmentation
    X. Zhan, Z. Liu, P. Luo, X. Tang, C. C. Loy
    in Proceedings of AAAI Conference on Artificial Intelligence, 2018 (AAAI, Spotlight)
    [PDF] [arXiv] [Project Page]
  • Unsupervised Learning of Discriminative Attributes and Visual Representations
    C. Huang, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR)
    [PDF] [Project Page]

Knowledge Distillation

  • Computation-Efficient Knowledge Distillation via Uncertainty-Aware Mixup
    G. Xu, Z. Liu, C. C. Loy
    Technical report, arXiv:2012.05217, 2020
    [arXiv] [Project Page]
  • Knowledge Distillation Meets Self-Supervision
    G. Xu, Z. Liu, X. Li, C. C. Loy
    European Conference on Computer Vision, 2020 (ECCV)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Residual Knowledge Distillation
    M. Gao, Y. Shen, Q. Li, C. C. Loy
    Technical report, arXiv:2002.09168, 2020
    [arXiv]
  • Inter-Region Affinity Distillation for Road Marking Segmentation
    Y. Hou, Z. Ma, C. Liu, T.-W. Hui, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Learning Lightweight Lane Detection CNNs by Self Attention Distillation
    Y. Hou, Z. Ma, C, Liu, C. C. Loy
    in Proceedings of International Conference on Computer Vision, 2019 (ICCV)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
    Y. Hou, Z. Ma, C. Liu, C. C. Loy
    in Proceedings of AAAI Conference on Artificial Intelligence, 2019 (AAAI, Oral)
    [PDF] [arXiv] [Project Page]
  • An Embarrassingly Simple Approach for Knowledge Distillation
    M. Gao, Y. Shen, Q. Li, C. C. Loy, X. Tang
    Technical report, arXiv:1812.01819, 2018
    [arXiv]

Continual Learning

  • Retrospective Class Incremental Learning
    Q. Tao, C. C. Loy, J. Cai, Z. Ge, S. See
    in Proceedings of IEEE International Conference on Multimedia and Expo, 2021 (ICME)
    [PDF]
  • Learning a Unified Classifier Incrementally via Rebalancing
    S. Hou, X. Pan, C. C. Loy, Z. Wang, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR)
    [PDF] [Project Page]
  • Lifelong Learning via Progressive Distillation and Retrospection
    S. Hou, X. Pan, C. C. Loy, Z. Wang, D. Lin
    in Proceedings of European Conference on Computer Vision, 2018 (ECCV)
    [PDF] [Project Page]

Long-Tailed Recognition

  • FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation
    Y Zang, C. Huang, C. C. Loy
    in Proceedings of IEEE/CVF International Conference on Computer Vision, 2021 (ICCV)
    [arXiv] [Project Page]
  • Learning Deep Representation for Imbalanced Classification
    C. Huang, Y. Li, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR, Spotlight)
    [PDF] [Project Page]

Domain Adaptation | Generalization

  • Semi-Supervised Domain Generalization with Stochastic StyleMatch
    K. Zhou, C. C. Loy, Z. Liu
    Technical report, arXiv:2106.00592, 2021
    [arXiv] [Project Page]
  • Domain Generalization in Vision: A Survey
    K. Zhou, Z. Liu, Y. Qiao, T. Xiang, C. C. Loy
    Technical report, arXiv:2103.02503, 2021
    [arXiv]
  • Sketch Me That Shoe
    Q. Yu, F. Liu, Y. Song, T. Xiang, T. M. Hospedales, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR, Oral)
    [PDF]

Network Compression

  • Network Pruning via Resource Reallocation
    Y. Hou, Z. Ma, C. Liu, Z. Wang, C. C. Loy
    Technical report, arXiv:2103.01847, 2021
    [arXiv]
  • EcoNAS: Finding Proxies for Economical Neural Architecture Search
    D. Zhou, X. Zhou, W. Zhang, C. C. Loy, S. Yi, X. Zhang, W. Ouyang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR)
    [PDF] [arXiv] [Supplementary Material]

Face Analysis

We are one of the earliest teams that investigates the use of deep learning in various face analysis applications such as face detection and face alignment.

Face Detection

  • WIDER Face and Pedestrian Challenge 2018: Methods and Results
    C. C. Loy, D. Lin, W. Ouyang, Y. Xiong, S. Yang, Q. Huang, D. Zhou, W. Xia, Q. Li, P. Luo, J. Yan, et al.
    Technical report, arXiv:1902.06854, 2019
    [arXiv] [Project Page]
  • Face Detection through Scale-Friendly Deep Convolutional Networks
    S. Yang, Y. Xiong, C. C. Loy, X. Tang
    Technical report, arXiv:1706.02863, 2017
    [arXiv] [Project Page]
  • Faceness-Net: Face Detection through Deep Facial Part Responses
    S. Yang, P. Luo, C. C. Loy, X. Tang
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 (TPAMI)
    [DOI] [arXiv] [Project Page]
  • WIDER FACE: A Face Detection Benchmark
    S. Yang, P. Luo, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR, Oral)
    [PDF] [arXiv] [Leaderboard | Dataset]
  • From Facial Part Responses to Face Detection: A Deep Learning Approach
    S. Yang, P. Luo, C. C. Loy, X. Tang
    in Proceedings of International Conference on Computer Vision, pp. 3676-3684, 2015 (ICCV)
    [PDF] [arXiv] [Supplementary Material] [Project Page]

Face Alignment

  • Unconstrained Face Alignment via Cascaded Compositional Learning
    S. Zhu, C. Li, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR)
    [PDF] [Project Page]
  • Learning Deep Representation for Face Alignment with Auxiliary Attributes
    Z. Zhang, P. Luo, C. C. Loy, X. Tang
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 5, pp. 918–930, 2016 (TPAMI)
    [DOI] [arXiv] [Project Page]
  • Face Alignment by Coarse-to-Fine Shape Searching
    S. Zhu, C. Li, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4998-5006, 2015 (CVPR)
    [PDF] [Project Page]
  • Towards Arbitrary-View Face Alignment by Recommendation Trees
    S. Zhu, C. Li, C. C. Loy, X. Tang
    Technical report, arXiv:1511.06627, 2015
    [arXiv]
  • An Empirical Study of Recent Face Alignment Methods
    H. Yang, X. Jia, C. C. Loy, P. Robinson
    Technical report, arXiv:1511.05049, 2015
    [arXiv] [Project Page]
  • Facial Landmark Detection by Deep Multi-task Learning
    Z. Zhang, P. Luo, C. C. Loy, X. Tang
    in Proceedings of European Conference on Computer Vision, pp. 94-108, 2014 (ECCV)
    [PDF] [arXiv] [Project Page]
  • Transferring Landmark Annotations for Cross-Dataset Face Alignment
    S. Zhu, C. Li, C. C. Loy, X. Tang
    Technical report, arXiv:1409.0602, 2014
    [PDF] [Project Page]

Face Attribute Recognition

  • Deep Imbalanced Learning for Face Recognition and Attribute Prediction
    C. Huang, Y. Li, C. C. Loy, X. Tang
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 (TPAMI)
    [DOI] [arXiv]
  • From Facial Expression Recognition to Interpersonal Relation Prediction
    Z. Zhang, P. Luo, C. C. Loy, X. Tang
    International Journal of Computer Vision, 2017 (IJCV)
    [DOI] [arXiv] [Project Page]
  • Quantifying Facial Age by Posterior of Age Comparisons
    Y. Zhang, L. Liu, C. Li, C. C. Loy
    in Proceedings of British Machine Vision Conference, 2017 (BMVC, Oral)
    [PDF] [arXiv] [Project Page]
  • Discriminative Sparse Neighbor Approximation for Imbalanced Learning
    C. Huang, C. C. Loy, X. Tang
    IEEE Transactions on Neural Networks and Learning Systems, 2017 (TNNLS)
    [DOI] [PDF] [arXiv]
  • Deep Learning Face Attributes for Face Detection and Alignment
    C. C. Loy, P. Luo, and C. Huang
    In R. S. Feris, C. Lampert and D. Parikh (Eds.), Visual Attributes, Springer, 2017
    [Book Link]
  • Learning Deep Representation for Imbalanced Classification
    C. Huang, Y. Li, C. C. Loy, X. Tang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR, Spotlight)
    [PDF] [Project Page]
  • Learning Social Relation Traits from Face Images
    Z. Zhang, P. Luo, C. C. Loy, X. Tang
    in Proceedings of International Conference on Computer Vision, pp. 3631-3639, 2015 (ICCV)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Cumulative Attribute Space for Age and Crowd Density Estimation
    K. Chen, S. Gong, T. Xiang, and C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467-2474, 2013 (CVPR, Oral)
    [PDF] [Poster] [Project Page]

Face Clustering

  • Learning to Cluster Faces via Confidence and Connectivity Estimation
    L. Yang, D. Chen, X. Zhan, R. Zhao, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR)
    [PDF] [arXiv] [Supplementary Material] [Project Page]
  • Learning to Cluster Faces on an Affinity Graph
    L. Yang, X. Zhan, D. Chen, J. Yan, C. C. Loy, D. Lin
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019 (CVPR, Oral)
    [PDF] [arXiv] [Project Page]
  • Merge or Not? Learning to Group Faces via Imitation Learning
    Y. He, K. Cao, C. Li, C. C. Loy
    in Proceedings of AAAI Conference on Artificial Intelligence, 2018 (AAAI, Spotlight)
    [PDF] [arXiv] [Project Page]
  • Joint Face Representation Adaptation and Clustering in Videos
    Z. Zhang, P. Luo, C. C. Loy, X. Tang
    in Proceedings of European Conference on Computer Vision, 2016 (ECCV)
    [PDF] [Project Page]

Face Recognition

  • Deep Imbalanced Learning for Face Recognition and Attribute Prediction
    C. Huang, Y. Li, C. C. Loy, X. Tang
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 (TPAMI)
    [DOI] [arXiv]
  • Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
    X. Zhan, Z. Liu, J. Yan, D. Lin, C. C. Loy
    in Proceedings of European Conference on Computer Vision, 2018 (ECCV)
    [PDF] [arXiv] [Project Page]
  • The Devil of Face Recognition is in the Noise
    F. Wang, L. Chen, C. Li, S. Huang, Y. Chen, C. Qian, C. C. Loy
    in Proceedings of European Conference on Computer Vision, 2018 (ECCV)
    [PDF] [arXiv] [Project Page]
  • Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
    K. Cao, Y. Rong, C. Li, X. Tang, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018 (CVPR)
    [PDF] [arXiv] [Project Page]
  • A Survey on Heterogeneous Face Recognition: Sketch, Infra-red, 3D and Low-Resolution
    S. Ouyang, T. Hospedales, Y. Song, X. Li, C. C. Loy, X. Wang
    Image and Vision Computing, 2016 (IVC)
    [DOI] [PDF]

Visual Surveillance

We investigate methods to analyze human behaviours across cameras and estimate crowd density.

Camera Network Analysis

  • Incremental Activity Modelling in Multiple Disjoint Cameras
    C. C. Loy, T. Xiang, S. Gong
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1799-1813, 2012 (TPAMI)
    [DOI] [PDF] [Project Page]
  • Comparing Visual Feature Coding for Learning Disjoint Camera Dependencies
    X. Zhu, S. Gong, and C. C. Loy
    in Proceedings of British Machine Vision Conference, 2012 (BMVC)
    [PDF] [Poster]
  • Security and Surveillance
    S. Gong, C. C. Loy, T. Xiang
    in T. Moeslund, A. Hilton, V. Krueger, L. Sigal (Eds.), Visual Analysis of Humans: Looking at People, Springer, pp. 455-472, 2011
    [DOI] [PDF]
  • Activity Understanding and Unusual Event Detection in Surveillance Videos
    C. C. Loy
    PhD Thesis, Queen Mary University of London, 2010
    [PDF]
  • Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding
    C. C. Loy, T. Xiang, S. Gong
    International Journal of Computer Vision, vol. 90, no. 1, pp. 106-129, 2010 (IJCV)
    [DOI] [PDF] [Project Page]
  • Modelling Activity Global Temporal Dependencies using Time Delayed Probabilistic Graphical Model
    C. C. Loy, T. Xiang, S. Gong
    in Proceedings of International Conference on Computer Vision, pp. 120-127, 2009 (ICCV, Oral)
    [PDF] [Project Page]
  • Multi-Camera Activity Correlation Analysis
    C. C. Loy, T. Xiang, S. Gong
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1988-1995, 2009 (CVPR, Oral)
    [PDF] [Project Page]

Person Re-Identification

  • Pedestrian Color Naming via Convolutional Neural Network
    Z. Cheng, X. Li, and C. C. Loy
    in Proceedings of Asian Conference on Computer Vision, 2016 (ACCV, Oral, Best Application Paper Honorable Mention)
    [PDF] [Project Page]
  • Human Attribute Recognition by Deep Hierarchical Contexts
    Y. Li, C. Huang, C. C. Loy, X. Tang
    in Proceedings of European Conference on Computer Vision, 2016 (ECCV)
    [PDF] [Project Page]
  • Pedestrian Attribute Recognition At Far Distance
    Y. Deng, P. Luo, C. C. Loy, X. Tang
    ACM Multimedia, pp. 789-792, 2014 (ACM MM)
    [PDF] [arXiv] [Project Page]
  • On-the-fly Feature Importance Mining for Person Re-Identification
    C. Liu, S. Gong, and C. C. Loy
    Pattern Recognition, vol. 47, no. 4, pp. 1602-1615, 2014 (PR)
    [DOI] [PDF] [Project Page]
  • Person Re-Identification
    S. Gong, M. Cristani, S. Yan, C. C. Loy (Eds.)
    Springer, January 2014
    [DOI] [Preface] [Introduction]
  • The Re-Identification Challenge
    S. Gong, M. Cristani, C. C. Loy, and T. Hospedales
    In Gong, Cristani, Yan, Loy (Eds.), Person Re-Identification, Springer, January 2014
    [PDF]
  • Evaluating Feature Importance for Re-Identification
    C. Liu, S. Gong, C. C. Loy, X. Lin
    In Gong, Cristani, Yan, Loy (Eds.), Person Re-Identification, Springer, January 2014
    [PDF]
  • POP: Person Re-Identification Post-Rank Optimisation
    C. Liu, C. C. Loy, S. Gong, and G. Wang
    in Proceedings of IEEE International Conference on Computer Vision, pp. 441-448, 2013 (ICCV)
    [PDF] [Poster] [Project Page]
  • Person Re-Identification by Manifold Ranking
    C. C. Loy, C. Liu, S. Gong
    in Proceedings of IEEE International Conference on Image Processing, pp. 3567-3571, 2013 (ICIP)
    [PDF] [Poster] [Project Page]
  • Person Re-Identification: What Features are Important?
    C. Liu, S. Gong, C. C. Loy, X. Lin
    in Proceedings of European Conference on Computer Vision, International Workshop on Re-Identification, pp. 391-401, 2012 (ECCVW)
    [PDF] [Project Page]

Crowd Behaviour Understanding and Profiling

  • Deep Learning for Scene Independent Crowd Analysis
    X. Wang and C. C. Loy
    In V. Murino, M. Cristani, S. Shah, S. Savarese (Eds.), Group and Crowd Behavior for Computer Vision, Elsevier, 2017
    [Book Link]
  • Crowded Scene Understanding by Deeply Learned Volumetric Slices
    J. Shao, C. C. Loy, X. Wang
    IEEE Transactions on Circuits and Systems for Video Technology, 2016 (TCVST)
    [DOI]
  • Slicing Convolutional Neural Network for Crowd Video Understanding
    J. Shao, C. C. Loy, K. Kang, X. Wang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016 (CVPR, Spotlight)
    [PDF] [Project Page]
  • Learning Scene-Independent Group Descriptors for Crowd Understanding
    J. Shao, C. C. Loy, K. Kang, X. Wang
    IEEE Transactions on Circuits and Systems for Video Technology, 2016 (TCVST)
    [DOI] [PDF]
  • Deeply Learned Attributes for Crowded Scene Understanding
    J. Shao, K. Kang, C. C. Loy, X. Wang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4657-4666, 2015 (CVPR, Oral)
    [PDF] [Project Page]
  • Scene-Independent Group Profiling in Crowd
    J. Shao, C. C. Loy, X. Wang
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2227-2234, 2014 (CVPR, Oral)
    [PDF] [Demo] [Project Page]
  • Stream-based Joint Exploration-Exploitation Active Learning
    C. C. Loy, T. M. Hospedales, T. Xiang, S. Gong
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1560-1567, 2012 (CVPR, Oral)
    [PDF] [Project Page]
  • Crowd Saliency Detection via Global Similarity Structure
    M. K. Lim, V. J. Kok, C. C. Loy, and C. S. Chan
    in Proceedings of International Conference on Pattern Recognition, pp. 3957-3962, 2014 (ICPR, Oral)
    [PDF]
  • Salient Motion Detection in Crowded Scenes
    C. C. Loy, T. Xiang, S. Gong
    in Proceedings of International Symposium on Communications, Control and Signal Processing, pp. 1-4, 2012
    [PDF]
  • Detecting and Discriminating Behavioural Anomalies
    C. C. Loy, T. Xiang, S. Gong
    Pattern Recognition, vol. 44, no. 1, pp. 117-132, 2011 (PR)
    [DOI] [PDF]
  • Stream-based Active Unusual Event Detection
    C. C. Loy, T. Xiang, S. Gong
    in Proceedings of Asian Conference on Computer Vision, pp. 161-175, 2010 (ACCV, Oral)
    [PDF]
  • Modelling Multi-object Activity by Gaussian Processes
    C. C. Loy, T. Xiang, S. Gong
    in Proceedings of British Machine Vision Conference, 2009 (BMVC)
    [PDF] [Extended Abstract] [Poster]
  • Surveillance Video Behaviour Profiling and Anomaly Detection
    C. C. Loy, T. Xiang, S. Gong
    in Proceedings of Society of Photo-Optical Instrumentation Engineers Conference Series, 2009
  • From Local Temporal Correlation to Global Anomaly Detection
    C. C. Loy, T. Xiang, S. Gong
    in Proceedings of European Conference on Computer Vision, International Workshop on Machine Learning for Vision-based Motion Analysis, 2008 (ECCVW)

Crowd Density Estimation

  • From Semi-Supervised to Transfer Counting of Crowds
    C. C. Loy, S. Gong, T. Xiang
    in Proceedings of IEEE International Conference on Computer Vision, pp. 2256-2263, 2013 (ICCV)
    [PDF] [Poster] [Project Page]
  • Cumulative Attribute Space for Age and Crowd Density Estimation
    K. Chen, S. Gong, T. Xiang, and C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467-2474, 2013 (CVPR, Oral)
    [PDF] [Poster] [Project Page]
  • Crowd Counting and Profiling: Methodology and Evaluation
    C. C. Loy, K. Chen, S. Gong, T. Xiang
    in S. Ali, K. Nishino, D. Manocha, and M. Shah (Eds.), Modeling, Simulation and Visual Analysis of Crowds, Springer, vol. 11, pp. 347-382, 2013
    [DOI] [PDF]
  • Feature Mining for Localised Crowd Counting
    K. Chen, C. C. Loy, S. Gong, T. Xiang
    in Proceedings of British Machine Vision Conference, 2012 (BMVC)
    [PDF] [Poster] [Project Page]

Video Summarization

  • Learning from Multiple Sources for Video Summarisation
    X. Zhu, C. C. Loy, S. Gong
    International Journal of Computer Vision, vol. 117, no. 3, pp. 247–268, 2015 (IJCV)
    [DOI] [PDF] [arXiv] [Project Page]
  • Constrained Clustering with Imperfect Oracles
    X. Zhu, C. C. Loy, S. Gong
    IEEE Transactions on Neural Networks and Learning Systems, 2015 (TNNLS)
    [DOI] [PDF] [Project Page]
  • Constructing Robust Affinity Graphs for Spectral Clustering
    X. Zhu, C. C. Loy, S. Gong
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1450-1457, 2014 (CVPR)
    [PDF] [Poster] [Project Page]
  • Video Synopsis by Heterogeneous Multi-Source Correlation
    X. Zhu, C. C. Loy, S. Gong
    in Proceedings of IEEE International Conference on Computer Vision, pp. 81-88, 2013 (ICCV)
    [PDF] [Poster] [Project Page]
  • Constrained Clustering: Effective Constraint Propagation with Imperfect Oracles
    X. Zhu, C. C. Loy, S. Gong
    in Proceedings of IEEE International Conference on Data Mining, pp. 1307-1312, 2013 (ICDM)
    [PDF] [Project Page]

Media Forensics

We collect large-scale datasets and develop new methods for face forgery detection.

Forgery Detection and Anti-Deepfake

  • DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
    L. Jiang, W. Wu, R. Li, C. Qian, C. C. Loy
    in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020 (CVPR)
    [PDF] [arXiv] [Supplementary Material] [Project Page]