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Meng Tang

Assistant Professor (Since 2023)
Department of Computer Science and Engineering
University of California, Merced

Email: mtang4 AT ucmerced.edu
Office: SE2 279

I work on computer vision and machine learning. I am particularly interested in developing data-efficient and compute-efficient models
by leveraging optimization methods, probabilistic graphical models, semi-supervised learning, generative AI etc.


GoogleScholar   GitHub   CV

Teaching
Students
Publication
Recent paper (2023 - )
  1. new paperScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
    Jacob Schnell, Jieke Wang, Lu Qi, Vincent Tao Hu, Meng Tang
    Workshop on SyntaGen at IEEE Conference on Computer Vision and Pattern Recognition (CVPR-W), June 2024. [PDF][Code]
  2. new paperLatent Space Editing in Transformer-Based Flow Matching
    Vincent Tao Hu, David W Zhang, Pascal Mettes, Meng Tang, Deli Zhao, Cees G.M. Snoek
    Annual AAAI Conference on Artificial Intelligence (AAAI), 2024. [PDF] [Code]  [Poster]
  3. Decepticon: Understanding Vulnerabilities of Transformers
    Mujahid Al Rafi, Yuan Feng, Fan Yao, Meng Tang, Hyeran Jeon
    IEEE International Symposium on Workload Characterization (IISWC), 2023. [PDF]
Earlier paper
  1. FroDO: From Detections to 3D Objects
    M. Rünz, K. Li, M. Tang, L. Ma, C. Kong, T. Schmidt, I. Reid, L. Agapito, J. Straub, S. Lovegrove, R. Newcombe
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [PDF] [Video]
  2. Beyond Gradient Descent for Regularized Segmentation Losses
    Dmitrii Marin, Meng Tang, Ismail Ben Ayed, Yuri Boykov
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [PDF] [Code]
  3. Kernel Cuts: Kernel and Spectral Clustering meet Regularization
    Meng Tang, Dmitrii Marin, Ismail Ben Ayed, Yuri Boykov
    International Journal of Computer Vision (IJCV), 2019. [PDF] [arXiv]
  4. Constrained-CNN losses for weakly supervised segmentation
    Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, Ismail Ben Ayed
    Medical Image Analysis (MedIA), 2019. [PDF] [Code] [MIDL 2018 version]
  5. On Regularized Losses for Weakly-supervised CNN Segmentation
    Meng Tang, Federico Perazzi, Abdelaziz Djelouah, Ismail Ben Ayed, Christopher Schroers, Yuri Boykov
    European Conference on Computer Vision (ECCV), 2018. [PDF] [Slides] [Code] [arXiv]
  6. Size-constraint loss for weakly supervised cnn segmentation
    Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, Ismail Ben Ayed
    Medical Imaging with Deep Learning (MIDL), 2018. [PDF] (oral)
  7. Normalized Cut Loss for Weakly-supervised CNN Segmentation
    Meng Tang, Abdelaziz Djelouah, Federico Perazzi, Yuri Boykov, Christopher Schroers
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [PDF] [Poster]
  8. Kernel Clustering: Density Biases and Solutions
    Dmitrii Marin, Meng Tang, Ismail Ben Ayed, Yuri Boykov
    IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2017. [PDF]
  9. Normalized Cut Meets MRF
    Meng Tang, Dmitrii Marin, Ismail Ben Ayed, Yuri Boykov
    European Conference on Computer Vision (ECCV), 2016. [PDF] [Code] [Poster] [Slides] [Video](oral)
  10. Secrets of GrabCut and Kernel K-means
    Meng Tang, Ismail Ben Ayed, Dmitrii Marin, Yuri Boykov
    IEEE International Conference on Computer Vision (ICCV), 2015. [PDF] [Code] [Poster]
  11. Pseudo-Bound Optimization for Binary Energies
    Meng Tang, Ismail Ben Ayed, Yuri Boykov
    European Conference on Computer Vision (ECCV), 2014. [PDF] [Code] [Poster] [Slides] [Video](oral)
  12. GrabCut in One Cut
    Meng Tang, Lena Gorelick, Olga Veksler, Yuri Boykov
    IEEE International Conference on Computer Vision (ICCV), 2013. [PDF] [Code] [Poster]
  13. Improved delay-range-dependent stability criteria for linear systems with interval time-varying delays
    Meng Tang, Yan-Wu Wang, Changyun Wen
    IET Control Theory & Applications, 2012. [PDF]