About Me

Hello! I am an Assistant Professor of Computer Science at NYU Courant. I am also affiliated with NYU Center for Data Science. Before that I was a research scientist at Facebook AI Research (FAIR), Menlo Park. I received my Ph.D. and M.S. degrees from CSE Department at UC San Diego, advised by Zhuowen Tu. During my PhD study, I also interned at NEC Labs, Adobe, Facebook, Google, DeepMind. Prior to that, I obtained my bachelor degree from Shanghai Jiao Tong University. My primary areas of interest in research are deep learning and computer vision. My goal is to develop improved representation learning techniques that aid machines in comprehending and utilizing massive amounts of structured information, as well as to push the boundaries of visual recognition by learning better representations at scale.

  • Started as an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at NYU. Multiple positions available!
  • Georgia and I are organizing the "Quo Vadis, Computer Vision?" workshop at ICCV 2023. See you in Paris.
  • Prospective PhD Students
    I'm seeking PhD candidates to join our group starting in Fall 2024. If you're interested in collaborating with me for your doctoral studies, please submit your application to the Courant CS Ph.D. program and be sure to include my name in your application materials.
    Internship opportunity
    From time to time, our group at NYU offers visiting researcher positions for individuals from different backgrounds. If you possess a strong passion for applying representation learning to tackle complex challenges in machine learning, computer vision (among many other exciting domains!), please feel free to send me an email with your CV.
    I am serving as an Area Chair for NeurIPS 2023, ECCV 2020/2022, ICCV 2021/2023, and CVPR 2021/2022, ICLR 2024.

    Research Group

    PhD Students
    Postdoc/Faculty Fellows
    • Sai Charitha Akula
    • Adithya Iyer
    • Willis Ma
    • Anh Ta
    • Austin Wang
    Student Visitors/Interns
    • Muzi Tao (SJTU)
    • Penghao Wu (UCSD)
    • Maryanne Xu (Yale)
    • Jihan Yang (HKU)
    • Jiraphon Yenphraphai (NYU)

    Selected Publications

    (* indicate equal contribution)
    Image Sculpting: Precise Object Editing with 3D Geometry Control
    ArXiv 2023
    Jiraphon Yenphraphai, Xichen Pan, Sainan Liu, Daniele Panozzo, Saining Xie
    Demystifying CLIP Data
    ICLR 2023
    Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, Christoph Feichtenhofer
    Spotlight Presentation
    Scalable Diffusion Models with Transformers
    ICCV 2023
    William Peebles, Saining Xie
    Oral Presentation
    CiT: Curation in Training for Effective Vision-Language Data
    ICCV 2023
    Hu Xu, Saining Xie, Po-Yao Huang, Licheng Yu, Russell Howes, Gargi Ghosh, Luke Zettlemoyer, Christoph Feichtenhofer
    Going Denser with Open-Vocabulary Part Segmentation
    ICCV 2023
    Peize Sun, Shoufa Chen, Chenchen Zhu, Fanyi Xiao, Ping Luo, Saining Xie, Zhicheng Yan
    ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
    CVPR 2023
    A ConvNet for the 2020s
    CVPR 2022
    SLIP: Self-supervision meets Language-Image Pre-training
    ECCV 2022
    Masked Feature Prediction for Self-Supervised Visual Pre-Training
    CVPR 2022
    Benchmarking Detection Transfer Learning with Vision Transformers
    arXiv 2021
    Masked Autoencoders are Scalable Vision Learners
    CVPR 2022
    Oral Presentation
    Pri3D: Can 3D Priors Help 2D Representation Learning?
    ICCV 2021
    An Empirical Study of Training Self-supervised Vision Transformers
    ICCV 2021
    Xinlei Chen*, Saining Xie*, Kaiming He,
    Oral Presentation
    On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness
    NeurIPS 2021
    Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
    CVPR 2021
    Oral Presentation
    Sample-Efficient Neural Architecture Search by Learning Action Space
    TPAMI 2021

    Graph Structure of Neural Networks
    ICML 2020
    PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
    ECCV 2020
    Spotlight Presentation
    Are Labels Necessary for Neural Architecture Search?
    ECCV 2020
    Spotlight Presentation
    Momentum Contrast for Unsupervised Visual Representation Learning
    CVPR 2020
    Best Paper Nomination (top 30)
    Decoupling Representation and Classifier for Long-Tailed Recognition
    ICLR 2020

    On Network Design Spaces for Visual Recognition
    ICCV 2019
    Exploring Randomly Wired Neural Networks for Image Recognition
    ICCV 2019
    Oral Presentation

    Deep Representation Learning with Induced Structural Priors
    Ph.D. Thesis, UC San Diego 2018
    Saining Xie
    Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
    ECCV 2018
    Attentional ShapeContextNet for Point Cloud Recognition
    CVPR 2018
    Saining Xie*, Sainan Liu*, Zeyu Chen, Zhuowen Tu
    Aggregated Residual Transformations for Deep Neural Networks
    CVPR 2017
    Top-down Learning for Structured Labeling with Convolutional Pseudoprior
    ECCV 2016
    Saining Xie*, Xun Huang*, Zhuowen Tu
    Holistically-Nested Edge Detection
    ICCV 2015
    Saining Xie, Zhuowen Tu
    Marr Prize Honorable Mention
    Deeply-Supervised Nets
    AISTATS 2015
    Chen-Yu Lee*, Saining Xie*, Patrick Gallagher*, Zhengyou Zhang, Zhuowen Tu
    Oral Presentation at the NeurIPS'14 Deep Learning Workshop
    Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification
    CVPR 2015