Jie(Jay) Mei
jiemei@uw.edu

I am a fifth-year Ph.D. from the Information Processing Lab at the University of Washington, Seattle where I am fortunate to be advised by Prof. Jenq-Neng Hwang. My research involves deep learning, lifelong learning, multimodal learning (vision+language), and 3D vision.

I just finished a real-time NeRF rendering project as a deep learrning research intern at Apple in 2023 summer. Before that, I was engaged in a vision language pre-training project as a research intern at Google Brain. In 2022 summer, I was a research scientist intern in the MapsCV team, Reality Labs, at Meta Platforms, Inc., working on panoptic segmentation of Lidar Point Clouds. I was also a software engineer intern in Megvii, China in 2019 summer, working on few-shot object detection.

Prior to my Ph.D. study, I was fortunate to be advised by Distinguished Prof. Demetri Terzopoulos during the UCLA CSST program. During my undergraduate, I am the recipient of the highest honor, the Principal 'Teli Xu' Scholarship, at Beijing Institute of Technology. I was also fortunate to be advised by Prof. Shengjin Wang from Tsinghua University on my graduation project.

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News


Work Experience

msft_logo 3D Vision, Apple Maps Deep Learning Research Intern (Jun, 2023 - Sep, 2023)
msft_logo Vision and Language Team, Google Brain Research Intern + Part-time
Student Researcher		 (Sep, 2022 - Apr, 2023)
msft_logo Maps CV Team, Reality Lab Research Scientist Intern (Jun, 2022 - Sep, 2022)
msft_logo Image and Video Group Software Engineer Intern (Jun, 2019 - Sep, 2019)

Research

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Scale-up NeRF Pipeline and Real-time Rendering
Jie Mei , Yongxi Lu, Weiyu Zhang

"In this project, we present a scale-up NeRF pipeline enabling real-time rendering on device."

@misc{mei2022unsupervised,
      title={Unsupervised Severely Deformed Mesh Reconstruction (DMR) from a Single-View Image},
      author={Jie Mei and Jingxi Yu and Suzanne Romain and Craig Rose and Kelsey Magrane and Graeme LeeSon and Jenq-Neng Hwang},
      year={2022},
      eprint={2201.09373},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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SLVP: Self-supervised Language-Video Pre-training for Referring Video Object Segmentation
Jie Mei , AJ Piergiovanni, Jenq-Neng Hwang, Wei Li
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024

"In this paper, we present a general self-supervised language-video pre-training (SLVP) strategy which brought non-negligible improvement to the downstream pixel-level Referring-VOS task."

arxiv/ bibtex 
@inproceedings{mei2024slvp,
  title={SLVP: Self-Supervised Language-Video Pre-Training for Referring Video Object Segmentation},
  author={Mei, Jie and Piergiovanni, AJ and Hwang, Jenq-Neng and Li, Wei},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={507--517},
  year={2024}
}
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HCIL: Hierarchical Class Incremental Learning for Longline Fishing Visual Monitoring
Jie Mei , Suzanne Romain, Craig Rose, Kelsey Magran, Jenq-Neng Hwang
IEEE International Conference on Image Processing (ICIP), 2022

"This work introduces a Hierarchical Class Incremental Learning (HCIL) model, which significantly improves the state-of-the-art hierarchical classification methods under the CIL scenario."

arxiv/ video / bibtex 
@misc{mei2022hcil,
      title={HCIL: Hierarchical Class Incremental Learning for Longline Fishing Visual Monitoring},
      author={Jie Mei and Suzanne Romain and Craig Rose and Kelsey Magrane and Jenq-Neng Hwang},
      year={2022},
      eprint={2202.13018},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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Unsupervised Severely Deformed Mesh Reconstruction (DMR) from a Single-View Image
Jie Mei , Jingxi Yu, Suzanne Romain, Craig Rose, Kelsey Magran, Graeme LeeSon, Jenq-Neng Hwang
IEEE International Conference on Multiedia and Expo (ICME), 2022

"This paper proposes an unsupervised mesh reconstruction method for severely deformed objects from a single-view image."

arxiv/ bibtex 
@misc{mei2022unsupervised,
      title={Unsupervised Severely Deformed Mesh Reconstruction (DMR) from a Single-View Image},
      author={Jie Mei and Jingxi Yu and Suzanne Romain and Craig Rose and Kelsey Magrane and Graeme LeeSon and Jenq-Neng Hwang},
      year={2022},
      eprint={2201.09373},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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Instance Tracking and Semantic Segmentation
Haotian Zhang, Yizhou Wang, Jie Mei , Cheng-Yen Yang, Jiarui Cai, Jenq-Neng Hwang
International Conference on Computer Vision (ICCV) Workshop , 2021

"This work achieved No.1 place in ICCV 2021 BMTT Challenge."

arxiv (KITTI) / arxiv (MOT) / bibtex 
@article{wanghvps,
  title={HVPS: A Human Video Panoptic Segmentation Framework},
  author={Wang, Yizhou and Zhang, Haotian and Jiang, Zhongyu and Mei, Jie and Yang, Cheng-Yen and Cai, Jiarui and Hwang, Jenq-Neng and Kim, Kwang-Ju and Kim, Pyong-Kun}
}
@article{zhangu3d,
  title={U3D-MOLTS: Unified 3D Monocular Object Localization, Tracking and Segmentation},
  author={Zhang, Haotian and Wang, Yizhou and Jiang, Zhongyu and Yang, Cheng-Yen and Mei, Jie and Cai, Jiarui and Hwang, Jenq-Neng and Kim, Kwang-Ju and Kim, Pyong-Kun}
}
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Absolute 3D Pose Estimation and Length Measurement of Severely Deformed Fish from Monocular Videos in Longline Fishing
Jie Mei , Jenq-Neng Hwang, Suzanne Romain, Craig Rose, Braden Moore, Kelsey Magrane
The international Conference on Acoustics, Speech, & Signal Processing (ICASSP), 2021

"This video-based method estimates the absolute 3D fish pose and fish length only from single-view 2D segmentation masks."

arxiv / video / bibtex 
@inproceedings{mei2021absolute,
  title={Absolute 3d Pose Estimation and Length Measurement of Severely Deformed Fish from Monocular Videos in Longline Fishing},
  author={Mei, Jie and Hwang, Jenq-Neng and Romain, Suzanne and Rose, Craig and Moore, Braden and Magrane, Kelsey},
  booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={2175--2179},
  year={2021},
  organization={IEEE}
}
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Video-based Hierarchical Species Classification for Longline Fishing Monitoring
Jie Mei , Jenq-Neng Hwang, Suzanne Romain, Craig Rose, Braden Moore, Kelsey Magrane
International Conference on Pattern Recognitiong (ICPR), 2020

"This paper proposes a hierarchical classification dataset and a method enforcing the hierarchical data structure. It also introduces an efficient training and inference strategy for video-based fisheries data classification."

arxiv / video / bibtex 
@inproceedings{Mei2020VideobasedHS,
  title={Video-based Hierarchical Species Classification for Longline Fishing Monitoring},
  author={J. Mei and Jenq-Neng Hwang and S. Romain and Craig S. Rose and Braden Moore and Kelsey Magrane},
  booktitle={ICPR Workshops},
  year={2020}
}

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