Mang Ye   叶 茫 Professor, School of Computer Science, Wuhan University Email: yemang AT whu.edu.cn, mangye16 AT gmail.com

   [Google Scholar]   [GitHub]   [DBLP] 

This page will not be updated. Please refer to the new webpage https://marswhu.github.io/index.html

News

• 2021.07, Two papers accepted by ICCV 2021 and two papers accepted by ACM MM 2021.

• 2020.08, We have released the code of dual-attentive cross-modality Re-ID in ECCV 2020. Code.

• 2020.07, The extended version of our CVPR19 paper on unsupervised embedding has been accepted by TPAMI. Code.

• 2020.01, A survey on deep person re-identification with a powerful AGW baseline. [ArXiv] [Code].

• 2019.04, We have released the code of unsupervised embedding learning in CVPR 2019. Code.

• 2018.05, We have released the code of visible-thermal (cross-modality) person re-identification in AAAI 2018 and IJCAI 2018. Code.

• 2017.09, We have released the code of unsupervised video person re-identification in ICCV 2017. Code.

Biography

• 2019-2020, Research Scientist, Inception Institute of Artificial Intelligence, Abu Dhabi, UAE.

• 2016-2019, Ph.D student, Hong Kong Baptist University, supervised by Prof. Pong C. Yuen.

• 2018.07-2018.12, Visiting scholar at Columbia University, working with Prof. Shih-Fu Chang.

• 2013-2016, MSc, NERCMS, Wuhan University, working with Dr. Chao Liang and Prof. Ruimin Hu .

• 2009-2013,B.E., Electronic Information Science and Technology, Wuhan University, China.

• My research interests include computer vision and multimedia analysis, particularly person re-identification and unsupervised feature learning.

Selected Publications

(* indicates corresponding author. Full list: Google Scholar)

2021

• Deep Learning for Person Re-identification: A Survey and Outlook
  Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, Steven C. H. Hoi.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
  [ PDF ] [ arXiv ] [ Code ] [ Bibtex]
  We propose a powerful AGW baseline for both single- and cross-modality Re-ID, and introduce a new evaluation metric mINP.

  @article{arxiv20reidsurvey,
    title={Deep Learning for Person Re-identification: A Survey and Outlook},
    author={Ye, Mang and Shen, Jianbing and Lin, Gaojie and Xiang, Tao and Shao, Ling and Hoi, Steven C. H},
    journal={arXiv preprint arXiv:2001.04193},
    year={2020},
  }
  

• Channel Augmented Joint Learning for Visible-Infrared Recognition
  Mang Ye, Weijian Ruan, Bo Du, Mike Zheng Shou.
  International Conference on Computer Vision (ICCV), 2021.
  [ PDF ] [ Bibtex] [ Code ]

  @inproceedings{iccv21caj,
      author    = {Ye, Mang and Ruan, Weijian and Du, Bo and Shou, Mike Zheng},
      title     = {Channel Augmented Joint Learning for Visible-Infrared Recognition},
      booktitle = {IEEE/CVF International Conference on Computer Vision},
      year      = {2021},
      pages     = {13567-13576}
  }
  

• Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation
  Xin Hao, Sanyuan Zhao, Mang Ye*, Jianbing Shen.
  International Conference on Computer Vision (ICCV), 2021.
  [ PDF ] [ Bibtex]

  @inproceedings{iccv21mclnet,
      author    = {Hao, Xin and Zhao, Sanyuan and Ye, Mang and Shen, Jianbing},
      title     = {Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation},
      booktitle = {IEEE/CVF International Conference on Computer Vision},
      year      = {2021},
      pages     = {16403-16412}
  }
  

• WePerson: Learning a Generalized Re-identification Model from All-weather Virtual Data
  He Li, Mang Ye*, Bo Du.
  ACM International Conference on Multimedia (ACM MM), 2021.
  [ PDF ] [ Bibtex] [ Code ]

  @inproceedings{mm21weperson,
    title={WePerson: Learning a Generalized Re-identification Model from All-weather Virtual Data},
    author={Li, He and Ye, Mang and Du, Bo},
    booktitle={ACM Multimedia},
    year={2021},
  }
  

• Cooperative Joint Attentive Network for Patient Outcome Prediction on Irregular Multi-Rate Multivariate Health Data
  Qingxiong Tan, Mang Ye*, Grace Lai-Hung Wong, Pong C. Yuen.
  International Joint Conference on Artificial Intelligence (IJCAI), 2021.
  [ PDF ] [ Bibtex]

  @inproceedings{ijcai21cajnet,
    title={Cooperative Joint Attentive Network for Patient Outcome Prediction on Irregular Multi-Rate Multivariate Health Data},
    author={Tan, Qingxiong and Ye, Mang and Wong, Grace Lai-Hung and Yuen, PongChi},
    booktitle={International Joint Conference on Artificial Intelligence},
    year={2021}
  }
  

• Person Re-Identification by Context-aware Part Attention and Multi-Head Collaborative Learning
  Dongming Wu, Mang Ye*, Gaojie Lin, Xin Gao, and Jianbing Shen.
  IEEE Transactions on Information Forensics and Security (TIFS), 2021.
  [ PDF ] [ Bibtex] [ Code ]
  

  @article{tifs21cpa,
    title={Person Re-Identification by Context-aware Part Attention and Multi-Head Collaborative Learning},
    author={Wu, Dongming and Ye, Mang and Lin, Gaojie and Gao, Xin and Shen, Jianbing},
    journal={IEEE Transactions on Information Forensics and Security (TIFS)},
    year={2021},
  }
  

• TICNet: A Target-Insight Correlation Network for Object Tracking
  Weijian Ruan, Mang Ye*, Yi Wu, Jun Chen, Wu Liu, Chao Liang, Ge Li, Chia-Wen Lin.
  IEEE Transactions on Cybernetics (TCYB), 2021.
  [ PDF ] [ Bibtex]

  @inproceedings{tcyb21track,
    title={TICNet: A Target-Insight Correlation Network for Object Tracking},
    author={Ruan, Weijian and Ye, Mang and Wu, Yi and Chen, Jun and Liu, Wu and Liang, Chao and Li, Ge and Lin, Chia-Wen},
    booktitle={IEEE Transactions on Cybernetics (TCYB)},
    year={2021}
  }
  

2020

• Augmentation Invariant and Instance Spreading Feature for Softmax Embedding
  Mang Ye, Jianbing Shen, Xu Zhang, Pong C. Yuen, Shih-Fu Chang.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
  [ PDF ] [ Bibtex] [ Code ] It's an extended version of our CVPR 19 paper.

  @article{pami20embedding,
    title={Augmentation Invariant and Instance Spreading Feature for Softmax Embedding},
    author={Ye, Mang and Shen, Jianbing and Zhang, Xu and Yuen, Pong C and Chang, Shih-Fu},
    journal={IEEE TPAMI},
    year={2020},
  }
  

• Probabilistic Structural Latent Representation for Unsupervised Embedding
  Mang Ye, Jianbing Shen.
  IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  [ PDF ] [ Bibtex] [ Code ]

  @inproceedings{cvpr20pslr,
    title={Probabilistic Structural Latent Representation for Unsupervised Embedding},
    author={Ye, Mang and Shen, Jianbing},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
    pages={5457--5466},
    year={2020},
  }
  

• Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
  Mang Ye, Jianbing Shen, David J. Crandall, Ling Shao, Jiebo Luo
  European Conference on Computer Vision (ECCV), 2020.
  [ PDF ] [ Bibtex] [ arXiv] [ Code ]

  @inproceedings{eccv20ddag,
    title={Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification},
    author={Ye, Mang and Shen, Jianbing and Crandall, David J. and Shao, Ling and Luo, Jiebo},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2020},
  }
  

• PurifyNet: A Robust Person Re-identification Model with Noisy Labels
  Mang Ye, Pong C. Yuen.
  IEEE Transactions on Information Forensics and Security(TIFS), 2020.
  [ PDF ] [ Bibtex]

  @article{tifs20noisy,
    title={PurifyNet: A Robust Person Re-identification Model with Noisy Labels},
    author={Ye, Mang and Yuen, Pong C.},
    journal={IEEE Transactions on Information Forensics and Security (TIFS)},
    volume={15},
    pages={2655--2666},
    year={2020},
  }
  

• Cross-Modality Person Re-Identification via Modality-aware Collaborative Ensemble Learning
  Mang Ye, Xiangyuan Lan, Qingming Leng, Jianbing Shen.
  IEEE Transactions on Image Processing (TIP), 2020.
  [ PDF ] [ Bibtex] [ Code ] It's an extended version of our ACM MM 19 paper.

  @article{tip20mace,
    title={Cross-Modality Person Re-Identification via Modality-aware Collaborative Ensemble Learning},
    author={Ye, Mang and Lan, Xiangyuan and Leng, Qingming and Shen, Jianbing},
    journal={IEEE Transactions on Image Processing (TIP)},
    year={2020},
  }
  

• Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learning
  Mang Ye, Jianbing Shen, Ling Shao.
  IEEE Transactions on Information Forensics and Security(TIFS), 2020.
  [ PDF ] [ Bibtex] [ Code ]

  @article{tifs20gray,
    title={Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learning},
    author={Ye, Mang and Shen, Jianbing and Shao, Ling},
    journal={IEEE Transactions on Information Forensics and Security (TIFS)},
    year={2020},
  }
  

• DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series
  Qingxiong Tan, Mang Ye, Baoyao Yang, Siqi Liu, Andy Jinhua Ma, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, Pong C. Yuen.
  AAAI Conference on Artificial Intelligence(AAAI), 2020.
  [ PDF ] [ Bibtex]

  @inproceedings{aaai20data,
    title={DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series},
    author={Tan, Qingxiong and Ye, Mang and Yang, Baoyao and Liu, Siqi and Ma, Andy Jinhua and Yip, Terry Cheuk-Fung and Wong, Grace Lai-Hung and Yuen, PongChi},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={34},
    number={01},
    pages={930--937},
    year={2020}
  }
  

• Explainable Uncertainty-Aware Convolutional Recurrent Neural Network for Irregular Medical Time Series
  Qingxiong Tan, Mang Ye, Andy Jinhua Ma, Baoyao Yang, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, Pong C. Yuen.
  IEEE Transactions on Neural Networks and Learning Systems(TNNLS), 2020.
  [ PDF ]

• Cross-domain Missingness-aware Time Series Adaptation with Similarity Distillation in Medical Applications
  Baoyao Yang,Mang Ye, Qingxiong Tan, Pong C. Yuen.
  IEEE Transactions on Cybernetics(TCYB), 2020.
  [ PDF ] [ Bibtex]

  @inproceedings{tcyb20time,
    title={Cross-domain Missingness-aware Time Series Adaptation with Similarity Distillation in Medical Applications},
    author={Yang, Baoyao and Ye, Mang and Tan, Qingxiong and Yuen, PongChi},
    booktitle={IEEE Transactions on Cybernetics (TCYB)},
    year={2020}
  }
  

• Modality-correlation-aware Sparse Representation for RGB-infrared Object Tracking
  Xiangyuan Lan, Mang Ye*, Shengping Zhang, Huiyu Zhou, Pong C Yuen.
  Pattern Recognition Letters (PRL), 2020. ESI Highly Cited paper.
  [ PDF ] [ Bibtex]

  @article{prl20modality,
    title={Modality-correlation-aware sparse representation for RGB-infrared object tracking},
    author={Lan, Xiangyuan and Ye, Mang and Zhang, Shengping and Zhou, Huiyu and Yuen, Pong C},
    journal={Pattern Recognition Letters},
    volume={130},
    pages={12--20},
    year={2020},
    publisher={Elsevier}
  }
  

2019

• Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
  Mang Ye, Xu Zhang, Pong C. Yuen, Shih-Fu Chang.
  IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  [ PDF ] [ Bibtex] [ Code ] [ arXiv ] [ Poster ]

  @inproceedings{cvpr19uel,
    title={Unsupervised Embedding Learning via Invariant and Spreading Instance Feature},
    author={Ye, Mang and Zhang, Xu and Yuen, Pong C and Chang, Shih-Fu},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={6210--6219},
    year={2019},
  }
  

• Dynamic Graph Co-Matching for Unsupervised Video-based Person Re-Identification
  Mang Ye, Jiawei Li, Andy J Ma, Liang Zheng, Pong C. Yuen.
  IEEE Transactions on Image Processing (TIP), 2019. ESI Highly Cited paper.
  [ PDF ] [ Bibtex] It's an extended version of our ICCV 17 paper.

  @article{tip19dgm,
    title={Dynamic graph co-matching for unsupervised video-based person re-identification},
    author={Ye, Mang and Li, Jiawei and Ma, Andy J and Zheng, Liang and Yuen, Pong C},
    journal={IEEE Transactions on Image Processing (TIP)},
    volume={28},
    number={6},
    pages={2976--2990},
    year={2019},
  }
  

• Bi-directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification
  Mang Ye, Xiangyuan Lan, Zheng Wang, Pong C. Yuen.
  IEEE Transactions on Information Forensics and Security (TIFS), 2019. ESI Highly Cited paper.
  [ PDF ] [ Bibtex] It's an extended version of our IJCAI 18 paper.

  @article{tifs19vtreid,
    title={Bi-directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification},
    author={Ye, Mang and Lan, Xiangyuan and Wang, Zheng and Yuen, Pong C},
    journal={IEEE Transactions on Information Forensics and Security (TIFS)},
    volume={15},
    pages={407-419},
    year={2020},
  }
  

• A Survey of Open-World Person Re-identification
  Qingming Leng*, Mang Ye*, Qi Tian.
  IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2019.
  [ PDF ] [ Bibtex]

  @article{tcsvt19survey,
    title={A Survey of Open-World Person Re-identification},
    author={Leng, Qingming and Ye, Mang and Tian, Qi},
    journal={IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)},
    year={2019},
    publisher={IEEE}
  }
  

• Improving Night-Time Pedestrian Retrieval with Distribution Alignment and Contextual Distance
  Mang Ye, Yi Cheng, Xiangyuan Lan, Hongyuan Zhu.
  IEEE Transactions on Industrial Informatics (TII), 2019. ESI Highly Cited paper.
  [ PDF ] [ Bibtex] [ Code ]

  @article{tii19bib,
    title={Improving Night-Time Pedestrian Retrieval with Distribution Alignment and Contextual Distance},
    author={Ye, Mang and Cheng, Yi and Lan, Xiangyuan and Zhu, Hongyuan},
    journal={IEEE Transactions on Industrial Informatics (TII)},
    year={2019},
    publisher={IEEE}
  }
  

• Modality-aware Collaborative Learning for Visible Thermal Person Re-Identification
  Mang Ye, Xiangyuan Lan, Qingming Leng.
  ACM International Conference on Multimedia (MM), 2019.
  [ PDF ] [ Bibtex]

  @inproceedings{mm19mac,
    title={Modality-aware Collaborative Learning for Visible Thermal Person Re-Identification},
    author={Ye, Mang and Lan, Xiangyuan and Leng, Qingming},
    booktitle={ACM Multimedia},
    year={2019},
  }
  

• Variation Generalized Feature Learning via Intra-view Variation Adaptation
  Jiawei Li, Mang Ye, Andy J. Ma, Pong C. Yuen.
  International Joint Conference on Artificial Intelligence (IJCAI), 2019.
  [ PDF] [ Bibtex]

  @inproceedings{ijcai19variation,
    title={Variation generalized feature learning via intra-view variation adaptation},
    author={Li, Jiawei and Ye, Mang and Ma, Andy J and Yuen, Pong C},
    booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
    pages={826--832},
    year={2019},
  }
  

• Learning Modality-consistency Feature Templates: A Robust RGB-infrared Tracking System
  Xiangyuan Lan, Mang Ye, Rui Shao, Bineng Zhong, Pong C Yuen, Huiyu Zhou.
  IEEE Transactions on Industrial Electronics (TIE), 2019. ESI Highly Cited paper.
  [ PDF ] [ Bibtex]

  @article{tie19track,
    title={Learning modality-consistency feature templates: A robust RGB-infrared tracking system},
    author={Lan, Xiangyuan and Ye, Mang and Shao, Rui and Zhong, Bineng and Yuen, Pong C and Zhou, Huiyu},
    journal={IEEE Transactions on Industrial Electronics},
    volume={66},
    number={12},
    pages={9887--9897},
    year={2019},
    publisher={IEEE}
  }
  

2018

• Robust Anchor Embedding for Unsupervised Video Person Re-Identification in the Wild
  Mang Ye, Xiangyuan Lan, Pong C. Yuen.
  European Conference on Computer Vision (ECCV), 2018.
  [ PDF ] [ Abstract] [ Bibtex] [ Code ]

  This paper addresses the scalability and robustness issues of estimating labels from imbalanced unlabeled data for unsupervised video-based person re-identification (re-ID). To achieve it, we propose a novel Robust AnChor Embedding (RACE) framework via deep feature representation learning for large-scale unsupervised video re-ID. Within this framework, anchor sequences representing different persons are firstly selected to formulate an anchor graph which also initializes the CNN model to get discriminative feature representations for later label estimation. To accurately estimate labels from unlabeled sequences with noisy frames, robust anchor embedding is introduced based on the regularized affine hull. Efficiency is ensured with $k$NN anchors embedding instead of the whole anchor set under manifold assumptions. After that, a robust and efficient top-$k$ counts label prediction strategy is proposed to predict the labels of unlabeled image sequences. With the newly estimated labeled sequences, the unified anchor embedding framework enables the feature learning process to be further facilitated. Extensive experimental results on the large-scale dataset show that the proposed method outperforms existing unsupervised video re-ID methods.

  @inproceedings{eccv18race,
    title={Robust Anchor Embedding for Unsupervised Video Person Re-Identification in the Wild},
    author={Ye, Mang and Lan, Xiangyuan and Yuen, Pong C.},
    booktitle={ECCV},
    year={2018},
  }
  

• Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking
  Mang Ye, Zheng Wang, Xiangyuan Lan, Pong C. Yuen.
  International Joint Conference on Artificial Intelligence (IJCAI), 2018.
  [ PDF ] [ Abstract] [ Bibtex] [ Code ]

  Cross-modality person re-identification between the thermal and visible domains is extremely important for night-time surveillance applications. Existing works in this filed mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, besides the cross-modality discrepancy caused by different camera spectrums, visible thermal person re-identification also suffers from large cross-modality and intra-modality variations caused by different camera views and human poses. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. It is advantageous in two aspects: 1) end-to-end feature learning directly from the data without extra metric learning steps, 2) it simultaneously handles the cross-modality and intra-modality variations to ensure the discriminability of the learnt representations. Meanwhile, identity loss is further incorporated to model the identity-specific information to handle large intra-class variations. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.

  @inproceedings{ijcai18vtreid,
    title={Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking},
    author={Ye, Mang and Wang, Zheng and Lan, Xiangyuan and Yuen, Pong C.},
    booktitle={IJCAI},
    year={2018},
  }
  

• Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification
  Zheng Wang, Mang Ye, Fan Yang, Xiang Bai, Shin’ichi Satoh.
  27th International Joint Conference on Artificial Intelligence (IJCAI), 2018.
  [ PDF ] [ Abstract] [ Bibtex] [ Code ]

  Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SRGAN is one of the most competitive image superresolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSRGAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.

  @inproceedings{wang2018cascaded,
    title={Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification.},
    author={Wang, Zheng and Ye, Mang and Yang, Fan and Bai, Xiang and Satoh, Shin'ichi},
    booktitle={IJCAI},
    year={2018}
  }
  

• Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification
  Mang Ye, Xiangyuan Lan, Jiawei Li, Pong C. Yuen.
  Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018.
  [ PDF] [ Abstract] [ Bibtex] [ Code ]

  Person re-identification is widely studied in visible spectrum, where all the person images are captured by visible cameras. However, visible cameras may not capture valid appearance information under poor illumination conditions, e.g, at night. In this case, thermal camera is superior since it is less dependent on the lighting by using infrared light to capture the human body. To this end, this paper investigates a cross-modal re-identification problem, namely visible-thermal person re-identification (VT-REID). Existing cross-modal matching methods mainly focus on modeling the cross-modality discrepancy, while VT-REID also suffers from cross-view variations caused by different camera views. Therefore, we propose a hierarchical cross-modality matching model by jointly optimizing the modality-specific and modality-shared metrics. The modality-specific metrics transform two heterogenous modalities into a consistent space that modality-shared metric can be subsequently learnt. Meanwhile, the modality-specific metric compacts features of the same person within each modality to handle the large intra-modality intra-person variations (e.g. viewpoints, pose). Additionally, an improved two-stream CNN network is presented to learn the multi-modality sharable feature representations. Identity loss and contrastive loss are integrated to enhance the discriminability and modality-invariance with partially shared layer parameters. Extensive experiments illustrate the effectiveness and robustness of the proposed method

  @inproceedings{aaai18vtreid,
    title={Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification},
    author={Ye, Mang and Lan, Xiangyuan and Li, Jiawei and Yuen, Pong C.},
    booktitle={AAAI},
    year={2018},
  }
  

• Robust Collaborative Discriminative Learning for RGB-Infrared Tracking
  Xiangyuan Lan, Mang Ye, Shengping Zhang, Pong C. Yuen.
  Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018.
  [ PDF] [ Bibtex]

  @inproceedings{aaai18robust,
    title={Robust collaborative discriminative learning for rgb-infrared tracking},
    author={Lan, Xiangyuan and Ye, Mang and Zhang, Shengping and Yuen, Pong C},
    booktitle={Thirty-Second AAAI Conference on Artificial Intelligence},
    year={2018}
  }
  

2017

• Dynamic Label Graph Matching for Unsupervised Video Re-Identification
  Mang Ye, Andy J Ma, Liang Zheng, Jiawei Li, Pong C. Yuen.
  IEEE International Conference on Computer Vision (ICCV), 2017.
  [PDF] [Abstract] [Bibtex] [ Code ] [ arXiv ]
  For updated results under standard evaluation protocol on MARS, please refer to the arXiv version and Github.
  

  Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in each camera, and then graph matching scheme is introduced for cross-camera labeling association. While labels directly output from existing graph matching methods may be noisy and inaccurate due to significant crosscamera variations, this paper propose a dynamic graph matching (DGM) method. DGM iteratively updates the image graph and the label estimation process by learning a better feature space with intermediate estimated labels. DGM is advantageous in two aspects: 1) the accuracy of estimated labels is improved significantly with the iterations; 2) DGM is robust to noisy initial training data. Extensive experiments conducted on three benchmarks including the large-scale MARS dataset show that DGM yields competitive performance to fully supervised baselines, and outperforms competing unsupervised learning methods.

  @inproceedings{iccv17dgm,
    title={Dynamic Label Graph Matching for Unsupervised Video Re-Identification},
    author={Ye, Mang and Ma, Andy J and Zheng, Liang and Li, Jiawei and Yuen, Pong C.},
    booktitle={ICCV},
    year={2017},
  }
  

2016 and before

• Person Re-identification via Ranking Aggregation of Similarity Pulling and Dissimilarity Pushing
  Mang Ye, Chao Liang, Yi Yu , Zheng Wang, Qingming Leng, Chunxia Xiao , Jun Chen, Ruimin Hu.
  IEEE Transactions on Multimedia (TMM), 2016.
  [PDF] [Abstract] [Bibtex] [Code] [Video]

  Person re-identification is a key technique to match different persons observed in non-overlapping camera views. Many researchers treat it as a special object retrieval problem, where ranking optimization plays an important role. Existing ranking optimization methods mainly utilize similarity relationship between the probe and gallery images to optimize the original ranking list, but seldom consider the important dissimilarity relationship. In this paper, we propose to use both similarity and dissimilarity cues in a ranking optimization framework for person re-identification. Its core idea is that the true match should not only be similar to those strongly similar galleries of the probe, but also be dissimilar to those strongly dissimilar galleries of the probe. Furthermore, motivated by the philosophy of multiview verification, a ranking aggregation algorithm is proposed to enhance the detection of similarity and dissimilarity based on the following assumption: the true match should be similar to the probe in different baseline methods. In other words, if a gallery blue image is strongly similar to the probe in one method, while simultaneously, strongly dissimilar to the probe in another method, it will probably be a wrong match of the probe. Extensive experiments conducted on public benchmark datasets and comparisons with different baseline methods have shown the great superiority of the proposed ranking optimization method.

  @inproceedings{ye2016rank,
    title={Person Re-identification via Ranking Aggregation of Similarity Pulling and Dissimilarity Pushing},
    author={Ye, Mang and Liang, Chao and Yu, Yi and et al.},
    booktitle={IEEE Transactions on Multimedia (TMM)},
    year={2016},
    organization={IEEE}
  }
  

• Ranking Optimization for Person Re-identification via Similarity and Dissimilarity
  Mang Ye, Chao Liang, Zheng Wang, Qingming Leng, Jun Chen.
  ACM International Conference on Multimedia (MM), 2015.
  [PDF] [Abstract] [Bibtex]

  Person re-identification is a key technique to match different persons observed in non-overlapping camera views. Many researchers treat it as a special object retrieval problem, where ranking optimization plays an important role. Existing ranking optimization methods utilize the similarity relationship between the probe and gallery images to optimize the original ranking list in which dissimilarity relationship is seldomly investigated. In this paper, we propose to use both similarity and dissimilarity cues in a ranking optimization framework for person re-identification. Its core idea is based on the phenomenon that the true match should not only be similar to the strong similar samples of the probe but also dissimilar to the strong dissimilar samples. Extensive experiments have shown the great superiority of the proposed ranking optimization method.

  @inproceedings{ye2015ranking,
    title={Ranking Optimization for Person Re-identification via Similarity and Dissimilarity},
    author={Ye, Mang and Liang, Chao and Wang, Zheng and Leng, Qingming and Chen, Jun},
    booktitle={Proceedings of the 23rd ACM international conference on Multimedia},
    pages={1239--1242},
    year={2015},
    organization={ACM}
  }
  

• Specific Person Retrieval via Incomplete Text Description
  Mang Ye, Chao Liang, Zheng Wang, Qingming Leng, Jun Chen, Jun Liu.
  International Conference on Multimedia Retrieval (ICMR), 2015.
  [PDF] [Abstract] [Bibtex]

  Searching for specific persons from surveillance videos captured by different cameras, is a key yet under-addressed challenge in multimedia system. Related person retrieval works mainly focus on searching person by visual appearance, known as person re-identification. However, the initial visual image may not be available in some practical applications. For example, the criminal is described by a text description indirectly, “A young woman wearing a red casual with a backpack”, the traditional methods can not conquer this issue. Based on a set of pre-defined attributes that the text description query can be transformed to an attribute vector, thus can be used to retrieval in the gallery set. And yet, the user-provided attributes are sometimes incomplete. This new issue is defined as Specific Person Retrieval via Incomplete Text Description. In this paper, we conduct a specific attribute completion to enrich the original text query and generate a more expressive attribute vector. Then, a pairwise-based metric learning is introduced for completed attribute vectors. Extensive experiments conducted on two benchmark datasets have shown our superior performance.

   @inproceedings{icmr_ym,
   title = {Specific Person Retrieval via Incomplete Text Description},
   author={Ye, Mang and Chao, Liang and Zheng, Wang and et al.},
   booktitle = {International Conference on Multimedia Retrieval (ICMR)},
   year = {2015},
   month = {June},
   address = {Shanghai, China}
  }
  

• Coupled-view Based Ranking Optimization for Person Re-identification
  Mang Ye, Jun Chen, Qingming Leng, Chao Liang, Zheng Wang and Kaimin Sun.
  International Conference on Multimedia Modelling (MMM), 2015. Oral.
  [PDF] [Abstract] [Bibtex]

  Person re-identification aims to match different persons observed in non-overlapping camera views. Researchers have proposed many person descriptors based on global or local descriptions, while both of them have achieved satisfying matching results, however, their ranking lists usually vary a lot for the same query person. These motivate us to investigate an approach to aggregate them to optimize the original matching results. In this paper, we proposed a coupled-view based ranking optimization method through cross KNN rank aggregation and graph-based re-ranking to revise the original ranking lists. Its core assumption is that the images of the same person should share the similar visual appearance in both global and local views. Extensive experiments on two datasets show the superiority of our proposed method with an average improvement of 20-30% over the state-of-the-art methods at CMC@1.

   @inproceedings{ye2015,
   title = {Coupled-view Based Ranking Optimization for Person Re-identification},
   author={Ye, Mang and Chen, Jun and Leng, Qingming and et al.},
   booktitle = {International Conference on Multimedia Modeling (MMM)},
   year = {2015},
   month = {Januray},
   address = {Sdyney, Australia}
  }
  

• WHU-NERCMS at TREVCID 2015:Instance search task
  Lei Yao, Mang Ye, Dongjing Liu, Rui Shao, Tao Liu, Jun Liu, Zheng Wang, Chao Liang.
  Participant Notebook Paper , TRECVID, 2015. (Ranked 4th /31 teams)
  [PDF] [Abstract] [Link] [Media Report]

  This paper introduces our work at the automatic instance search task of TRECVID 2015. The purpose of this task is to search specific targets in a large-scale video database. The key problems this year we are concerned about includes: 1. How to improve traditional BoW models; 2. How to improve retrieval precision with cross-mode information as an auxiliary to traditional visual features; 3. How to optimize the initial retrieval results. Correspondingly, our work is divided into three parts: First part is object retrieval with visual features based on BoW model. In this part, Bow model is augmented by Query Adaptive Similarity Measure. Second part is object retrieval with textual information. We adopt caption information and plot information of the series EastEnders in its official websites . Third part includes several fusion and optimization strategies adopted to improve the initial results. The main improvement in the strategies is Query Expansion With Adjacent Shots, which aims to retrieve more precisely in the adjacent shots of initial top-k results.

• WHU-NERCMS at TREVCID 2014:Instance search task
  Mang Ye, Bingyue Huang, Lei Yao, et al.
  Participant Notebook Paper , TRECVID, 2014.
  [PDF] [Abstract] [Link]

  This paper introduces our work at the automatic instance search task of TRECVID 2014. Our work is divided into two parts: First part is object retrieval based on BOW. Specially, we extract feature histogram of frames through general BoW. We adopt similarity measure method to compare the probe and gallery shots, then we obtain the initial ranking results; Second, several optimization strategies are adopted to improve the initial results.

Professional Services

Associate Editor:

• Journal of Electronic Imaging

Invited PC Member/Reviewer for conferences:

• ICCV 2019 2021, CVPR 2018 2019 2020 2021, ECCV 2020, IJCAI 2017 2018, AAAI 2018 2020 2021, ICPR 2018, ACCV 2018, BMVC 2019 2020 2021, WACV 2021 2022

• IJCAI 2019 Session Chair

• ICME 2021 Area Chair

Invited Reviewer for journals:

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

• IEEE Transactions on Image Processing (TIP)

• IEEE Transactions on Multimedia (TMM)

• IEEE Transactions on Biometrics, Behavior, and Identity Science (TBIOM)

• Pattern Recognition (PR)

• Computer Vision and Image Understanding (CVIU)

• IEEE Access

• Neurocomputing (Outstanding Reviewer Award)

• Pattern Recognition Letters (PRL) (Outstanding Reviewer Award)

Competitions

• 2015.5-2015.8, TRECVID 2015: Instance search task. We ranked 4th over 31 participants.

• 2014.6-2014.9, Multi-camera Tracking via Person Re-identification, National Graduate Contest on Smart-City Technology and Creative Design. We ranked 3rd in the Final.

• 2014.5-2014.8, TRECVID 2014: Instance search task. I worked as a group leader.

Awards

• 2019, Yakun Scholarship Scheme for Mainland Postgraduate Students

• 2019, Excellent Teaching Assistant Performance Award

• 2016-2018, Computer Science Department RPg Performance Award

• 2016-2019, Hong Kong PhD Fellowship

• 2016, Academic Breakthrough Prize awarded by NERCMS

• 2015, National Scholarship

• 2015, Google Excellence Scholarship

• 2014, National Scholarship

• 2014, 3rd Prize in 1st National Graduate Contest on Smart-City Technology and Creative Design

Teaching Assistant

• COMP7870, IT Innovation Management and Entrepreneurship [2018-19 S2]

• COMP4005, Information Systems Theory, Methodology and Architecture [2017-18 S1]

• COMP7400, Financial Analysis and Decision Making [2016-17-18 S2]

• COMP7800, Analytic Models in IT Management [2016-17 S1]

• COMP2005, Business in the IT Context [2016-17 S1]

     

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