Ailing Zeng 曾爱玲

Ph.D. Department of Computer Science and Engineering The Chinese University of Hong Kong Visiting Scholar Robotics Institute, Carnegie Mellon University Google Scholar Resume

Biography

I obtained my Ph.D. degree from the department of computer science and engineering, the Chinese University of Hong Kong, supervised by Prof. Qiang Xu. I was a visiting scholar in the Robotics Institute, Carnegie Mellon University. I have also spent wonderful time at Baidu, Microsoft Asia, and Sensetime as a research intern. Before that I got my bachelor’s degrees (Top 1% graduates) of Electronic Engineering and Finance mathematics in Xiamen University.

My research interests include: 1) human-centric visual understanding by deep learning, like 3d human pose estimation/reconstruction/prediction/animation; 2) human-centric image/motion generation, neural rendering, and editing; 3) time series analysis.

I'm looking for self-motivated interns at International Digital Economy Academy (IDEA). If you are interested in above topics, please send me your resume by email.

News

• [2022.11] LTSF-Linear has been accepted to AAAI 2023 with three strong accept!

• [2022.09] SCINet has been accepted to NeurIPS 2022!

• [2022.09] Check out our recent paper on ViT Feature Knowledge Distillation.

• [2022.07] Invited Talk at OpenMMlab about Human Motion Modeling from a Time Series Perspective. [Slides] [Video]

• [2022.07] Three papers have been accepted to ECCV 2022! (1 oral and 2 posters)

• [2022.01] T-WaveNet has been accepted to ICLR 2022!

• [2021.07] Two papers accepted to ICCV 2021! (1 oral and 1 poster)

• [2021.05] One paper accepted to IJCAI 2021!

• [2020.07] One paper accepted to ECCV 2020!

Publications

Are Transformers Effective for Time Series Forecasting? [Code] [知乎] Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023 We try to question the validity of Transformer-based time series forecasting solutions via a linear layer.

DeciWatch: A Simple Baseline for 10× Efficient 2D and 3D Pose Estimation [Website] [Code] [Data] Ailing Zeng, Xuan Ju, Lei Yang, Ruiyuan Gao, Xizhou Zhu, Bo Dai, Qiang Xu European Conference on Computer Vision (ECCV), 2022 DeciWatch is a simple baseline framework with the sample-denoise-recover scheme for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation.

SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos [Website] [Code] [知乎] Ailing Zeng, Lei Yang, Xuan Ju, Jiefeng Li, Jianyi Wang, Qiang Xu European Conference on Computer Vision (ECCV), 2022 SmoothNet is a refinement network, which can be attached to any existing pose estimators to improve their temporal smoothness and enhance the per-frame precision.

HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling [Website] Zhongang Cai*, Daxuan Ren*, Ailing Zeng*, Zhengyu Lin*, Tao Yu*, Wenjia Wang*, Xiangyu Fan, Yang Gao, Yifan Yu, Liang Pan, Fangzhou Hong, Mingyuan Zhang, Chen Change Loy, Lei Yang^, Ziwei Liu^ (Oral) European Conference on Computer Vision (ECCV), 2022 HuMMan is a large-scale multi-modal 4D human dataset with 1000 human subjects, 400k sequences and 60M frames.

SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction [Code] Minhao Liu, Ailing Zeng, Qiuxia Lai, Qiang Xu Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022 We propose a novel and general CNN-based backbone for the time series forecasting problem, achieving SOTAs on several real-world datasets.

T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis [Code] Minhao Liu, Ailing Zeng, Qiuxia Lai, Qiang Xu 2022 Tenth International Conference on Learning Representations (ICLR'22) We introduce a tree-structured wavelet deep neural network to effectively extract more discriminative and expressive feature representations in time series signals.

Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation [Code] Ailing Zeng, Xiao Sun, Lei Yang, Nanxuan Zhao, Minhao Liu, Qiang Xu 2021 IEEE/CVF International Conference on Computer Vision (ICCV’21) We explore a novel skeletal graph neural network to deal with hard poses in 3D pose estimation.

Human Pose Regression with Residual Log-likelihood Estimation [Code] [知乎] Jiefeng Li, Siyuan Bian, Ailing Zeng, Can Wang, Bo Pang, Wentao Liu and Cewu Lu (Oral) 2021 IEEE/CVF International Conference on Computer Vision (ICCV’21) We propose a novel regression paradigm, which is superior to the heatmap-based methods for the first time, especially on multi-person pose estimation.

SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach [Code] Ailing Zeng, Xiao Sun, Fuyang Huang, Minhao Liu, Qiang Xu, Stephen Lin 2020 European Conference on Computer Vision (ECCV’20) We design a split-and-recombine approach to improve generalization performance in 3D human pose estimation, especially on rare and unseen poses.

Working Experience

• Research intern at Sensetime Research, X-lab. (Nov. 2020 to Apr. 2022)
  Worked on robust and efficient 2D/3D human pose estimation&reconstruction from videos.

• Research intern at Microsoft Asia Research, Visual Computing Group. (Dec. 2019 to Sep. 2020)
  Worked on improving the generalization ability of single-view 3D human pose estimation from either images or videos.

• Research intern at Baidu Research. (June 2018 to Sep. 2018)
  Worked on 2D human pose estimation.

• TMT investment intern at ASB Ventures. (March 2017 to Aug. 2017)
  Analyzed technological feasibility of start-up companies.

• In recent years, I am a reviewer of the following conferences and journals: CVPR, ICCV, ICML, NeurIPS, AAAI, IJCV.

Honors & Awards

• Full Postgraduate Studentship, CUHK (2017 - 2021)

• Excellent League Member, Top 1% student of Xiamen University

• National Scholarship (2015)