Wanhua Li

I am an incoming postdoctoral fellow at Harvard University supervised by Prof. Hanspeter Pfister in Fall 2022. Prior to that, I received my Ph.D. from the Department of Automation at Tsinghua University in 2022.

If you are interested in my research or would like to work with me as an intern at Harvard University, feel free to contact me. Remote collaboration is also welcome!

Email: wanhua016 [AT] gmail [DOT] com
li-wh17 [AT] mails [DOT] tsinghua [DOT] edu [DOT] cn

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News

  • 2022-09: One paper on language-guided ordinal regression is accepted by NeurIPS 2022.
  • 2022-07: Two papers on multi-attribute learning and talking head synthesis are accepted by ECCV 2022.
  • 2022-06: One paper on age estimation is accepted by TIP.
  • 2022-04: One paper on image inpainting is accepted by TMM.
  • 2021-10: Our team won the 3rd place in 2021 VIPriors Instance Segmentation Challenge (ICCV 2021).
  • 2021-07: One paper on video inpainting detection is accepted by ICCV 2021.
  • 2021-04: One paper on kinship verification is accepted by TIP.
  • 2021-03: Three papers on uncertainty learning, kinship verification, and face clustering are accepted to CVPR 2021.
  • 2020-07: One paper on social relation recognition is accepted to ECCV 2020.
  • 2020-03: One paper on kinship verification is accepted as oral presentation at ICME 2020.
  • 2019-02: One paper on age estimation is accepted to CVPR 2019.
  • Selected Publications

    dise Label2Label: A Language Modeling Framework for Multi-Attribute Learning
    Wanhua Li, Zhexuan Cao, Jianjiang Feng, Jie Zhou, and Jiwen Lu
    European Conference on Computer Vision (ECCV), 2022
    [Website] [arxiv] [Video] [Code]

    We propose a language modeling framework named Label2Label to model the complex instance-wise attribute relations, which regards each attribute label as a “word” and recovers the label “sentence” based on the masked one.

    dise Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
    Shuai Shen, Wanhua Li, Zheng Zhu, Yueqi Duan, Jie Zhou, and Jiwen Lu
    European Conference on Computer Vision (ECCV), 2022
    [Website] [arxiv] [Video] [Code]

    We propose dynamic facial radiance fields conditioned on the 3D aware reference image features. The facial field can rapidly generalize to novel identities with only 15s clip.

    dise MetaAge: Meta-Learning Personalized Age Estimators
    Wanhua Li, Jiwen Lu, Abudukelimu Wuerkaixi, Jianjiang Feng, and Jie Zhou
    IEEE Transactions on Image Processing, 2022
    [Website] [Paper] [arxiv] [Code]

    We propose a personalized age estimation method named MetaAge, which learns the mapping from identity information to age estimator parameters.

    dise Learning Adaptive Patch Generators for Mask-Robust Image Inpainting
    Hongyi Sun, Wanhua Li, Yueqi Duan, Jie Zhou, and Jiwen Lu
    IEEE Transactions on Multimedia, 2022
    [Paper] [bibtex]

    We propose a Mask-Robust Inpainting Network (MRIN) to recover the masked areas of an image with a patch-wise inpainting process.

    dise Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical
    Wanhua Li, Jiwen Lu, Abudukelimu Wuerkaixi, Jianjiang Feng, and Jie Zhou
    IEEE Transactions on Image Processing, 2021
    [Paper] [arxiv] [bibtex]

    We develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity for graph-based kinship verification.

    dise Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection
    Bingyao Yu, Wanhua Li, Xiu Li, Jiwen Lu, and Jie Zhou
    IEEE International Conference on Computer Vision (ICCV), 2021
    [Paper] [bibtex]

    We propose a Frequency-Aware Spatiotemporal Transformer for video inpainting detection, which simultaneously mines the traces of video inpainting from spatial, temporal, and frequency domains.

    dise Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
    Wanhua Li, Xiaoke Huang, Jiwen Lu, Jianjiang Feng, and Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
    [Website] [arxiv] [Video] [Code]

    We propose probabilistic ordinal embeddings to empower the present-day regression methods with the ability of uncertainty estimation.

    dise Meta-Mining Discriminative Samples for Kinship Verification
    Wanhua Li, Shiwei Wang, Jiwen Lu, Jianjiang Feng, and Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
    [Website] [arxiv] [Video] [bibtex]

    A Discriminative Sample Meta-Mining strategy is proposed to mine discriminative information from limited positive pairs and sufficient negative samples for kinship verification.

    dise Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes
    Shuai Shen, Wanhua Li, Zheng Zhu, Guan Huang, Dalong Du, Jiwen Lu, and Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
    [Website] [arxiv] [Code] [Video]

    It is the first face clustering method to train on very large-scale graph with 20M nodes, and achieve superior inference results on 12M testing data.

    dise Graph-Based Social Relation Reasoning
    Wanhua Li, Yueqi Duan, Jiwen Lu, Jianjiang Feng, and Jie Zhou
    European Conference on Computer Vision (ECCV), 2020
    [Website] [arxiv] [Video] [Code]

    A simpler, faster, and more accurate method for social relation recognition.

    dise Graph-based Kinship Reasoning Network
    Wanhua Li, Yingqiang Zhang, Kangchen Lv, Jiwen Lu, Jianjiang Feng, Jie Zhou
    IEEE International Conference on Multimedia and Expo (ICME), 2020
    Oral Presentation
    [arXiv] [Video] [bibtex]

    We considers how to compare and fuse the extracted feature pair to reason about the kin relations with the proposed graph-based kinship reasoning networks.

    dise BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation
    Wanhua Li, Jiwen Lu, Jianjiang Feng, Chunjing Xu, Jie Zhou, Qi Tian
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
    [arXiv] [PDF] [bibtex]

    We propose BridgeNet for age estimation, which aims to mine the continuous relation between age labels effectively.

    Honors and Awards

  • ICCV Doctoral Consortium Travel Award, 2021.
  • Weihai Talent Scholarship, Tsinghua, 2021.
  • 3rd Place in 2021 VIPriors Instance Segmentation Challenge @ICCV 2021.
  • 2nd Place in ChaLearn LAP Large-scale Isolated Gesture Recognition Challenge @ICCV 2017.
  • Outstanding Undergraduate Thesis, SYSU, 2017.
  • Outstanding Graduate, SYSU, 2017.
  • National Encouragement Scholarship, Ministry of Education of P.R. China, 2016.
  • National Scholarship, Ministry of Education of P.R. China, 2015.
  • National Scholarship, Ministry of Education of P.R. China, 2014.
  • Professional Activities

  • Reviewer, IEEE Transactions on Image Processing.
  • Reviewer, IEEE Transactions on Neural Networks and Learning Systems.
  • Reviewer, IEEE Transactions on Circuits and Systems for Video Technology.
  • Reviewer, IEEE Transactions on Biometrics, Behavior, and Identity Science.
  • Reviewer, IEEE Transactions on Cybernetics.
  • Reviewer, Pattern Recognition.
  • Reviewer, Neural Networks.
  • Reviewer, Neurocomputing.
  • Reviewer, Pattern Recognition Letters.
  • Reviewer, Journal of Visual Communication and Image Representation.
  • Reviewer, Knowledge-Based Systems.
  • Reviewer, International Conference on Computer Vision (ICCV), 2021.
  • Reviewer, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  • Reviewer, European Conference on Computer Vision (ECCV), 2022.
  • Reviewer, IEEE International Conference on Multimedia and Expo (ICME), 2019-2022.
  • Reviewer, IEEE International Conference on Image Processing (ICIP), 2018-2022.
  • Reviewer, International Conference on Pattern Recognition (ICPR), 2018-2022.
  • Reviewer, Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 2021-2022.

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