PUBLICATIONS

Publications by Theme:

Google Scholar Citations

Machine learning

  • FeatUp: A Model-Agnostic Framework for Features at Any Resolution
    Stephanie Fu, Mark Hamilton, Laura Brandt, Axel Feldman, Zhoutong Zhang, William T. Freeman
    International Conference on Learning Representations (ICLR) 2024
    Paper (pdf)
  • Diffusion with forward models: Solving stochastic inverse problems without direct supervision
    Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Josh Tenenbaum, Frédo Durand, Bill Freeman, Vincent Sitzmann
    Advances in Neural Information Processing Systems 2023
    Paper (pdf)
  • Separating the” Chirp” from the” Chat”: Self-supervised Visual Grounding of Sound and Language
    Mark Hamilton, Andrew Zisserman, John R Hershey, William T Freeman
    IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024
    Paper (pdf)
  • Alchemist: Parametric control of material properties with diffusion models
    Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, Bill Freeman, Mark Matthews
    IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024
    Paper (pdf)
  • One-step diffusion with distribution matching distillation
    Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, Taesung Park
    IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024
    Paper (pdf)
  • Muse: Text-To-Image Generation via Masked Generative Transformers
    Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Patrick Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan
    International Conference on Machine Learning 2023
    Paper (pdf)
  • Associating objects and their effects in video through coordination games
    Erika Lu, Forrester Cole, Weidi Xie, Tali Dekel, Bill Freeman, Andrew Zisserman, Michael Rubinstein
    Advances in Neural Information Processing Systems 2022
    Paper (pdf)
  • 3d motion magnification: Visualizing subtle motions from time-varying radiance fields
    Brandon Y Feng, Hadi Alzayer, Michael Rubinstein, William T Freeman, Jia-Bin Huang
    IEEE/CVF International Conference on Computer Vision 2023
    Paper (pdf)
  • Score-based diffusion models as principled priors for inverse imaging
    Berthy T Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L Bouman, William T Freeman
    IEEE/CVF International Conference on Computer Vision 2023
    Paper (pdf)
  • Unsupervised semantic segmentation by distilling feature correspondences
    Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, William T Freeman
    International Conference on Learning Representations (ICLR) 2022
    Paper (pdf)
  • Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
    Mark Hamilton, Scott Lundberg, Lei Zhang, Stephanie Fu, William T Freeman
    International Conference on Learning Representations (ICLR) 2022
    Paper (pdf)
  • What you can learn by staring at a blank wall
    Prafull Sharma, Miika Aittala, Yoav Y Schechner, Antonio Torralba, Gregory W Wornell, William T Freeman, Frédo Durand
    International Conference on Computer Vision (ICCV) 2021
    Paper (pdf)
  • Large-scale intelligent microservices
    Mark Hamilton, Nick Gonsalves, Christina Lee, Anand Raman, Brendan Walsh, Siddhartha Prasad, Dalitso Banda, Lucy Zhang, Lei Zhang, William T Freeman
    IEEE International Conference on Big Data (Big Data) 2020
    Paper (pdf)
  • Multi-plane program induction with 3d box priors
    Yikai Li, Jiayuan Mao, Xiuming Zhang, Bill Freeman, Josh Tenenbaum, Noah Snavely, Jiajun Wu
    Advances in Neural Information Processing Systems (NeurIPS) 2020
    Paper (pdf)
  • Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows
    Andrei Zanfir, Eduard Gabriel Bazavan, Hongyi Xu, Bill Freeman, Rahul Sukthankar, Cristian Sminchisescu
    European Conference on Computer Vision (ECCV), 2020
    Paper (pdf)
  • GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models
    Hongyi Xu, Eduard Gabriel Bazavan, Andrei Zanfir, William T Freeman, Rahul Sukthankar, Cristian Sminchisescu
    Conference on Computer Vision and Pattern Recognition (CVPR), 2020
    Paper (pdf)
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    Project Repo
  • Perspective Plane Program Induction From a Single Image
    Yikai Li, Jiayuan Mao, Xiuming Zhang, William T Freeman, Joshua B Tenenbaum, Jiajun Wu
    Computer Vision and Pattern Recognition(CVPR), 2020
    Paper (pdf)
  • SpeedNet: Learning the Speediness in Videos
    Sagie Benaim, Ariel Ephrat, Oran Lang, Inbar Mosseri, William T Freeman, Michael Rubinstein, Michal Irani, Tali Dekel
    Conference on Computer Vision and Pattern Recognition (CVPR), 2020
    Paper (pdf)
  • Semantic Pyramid for Image Generation
    Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T Freeman, Tali Dekel
    Computer Vision and Pattern Recognition (CVPR), 2020
    Paper (pdf)
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    Project Page
  • Deep Audio Priors Emerge From Harmonic Convolutional Networks
    Zhoutong Zhang, Yunyun Wang, Chuang Gan, Jiajun Wu, Joshua B Tenenbaum, Antonio Torralba, William T Freeman
    International Conference on Learning Representations (ICLR), 2019
    Paper (pdf)
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    Project Page
  • Reasoning about physical interactions with object-centric models
    Michael Janner, Sergey Levine, William T Freeman, Joshua B Tenenbaum, Chelsea Finn, Jiajun Wu
    International Conference on Learning Representations (ICLR), 2019
    Paper (pdf)
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    Project Page
  • Boundless: Generative adversarial networks for image extension
    Piotr Teterwak, Aaron Sarna, Dilip Krishnan, Aaron Maschinot, David Belanger, Ce Liu, William T Freeman
    IEEE International Conference on Computer Vision(ICCV), 2019
    Paper (pdf)
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    Project Page
  • Learning shape templates with structured implicit functions
    Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T Freeman, Thomas Funkhouser
    IEEE International Conference on Computer Vision(ICCV), 2019
    Paper (pdf)
  • Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
    Tianfan Xue, Jiajun Wu, Katherine L. Bouman, and William T. Freeman
    IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2019
    Paper (pdf)
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    Project page
  • Best-Buddies Similarity for Robust Template Matching
    Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, William T. Freeman
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
    Paper (pdf)
  • Shape Recipes: Scene Representations that Refer to the Image
    William T. Freeman, Antonio Torralba
    Neural Information Processing Systems (NIPS) 2002
    Paper (pdf)
  • Group Norm for Learning Structured SVMs with Unstructured Latent Variables
    Daowen Chen, Dhruv Batra, William T. Freeman
    2013 IEEE International Conference on Computer Vision (ICCV)
    Paper (pdf)
  • Bayesian decision theory, the maximum local mass estimate, and color constancy
    W. T. Freeman and D. H. Brainard
    Fifth International Conference on Computer Vision, IEEE Computer Society, Cambridge, MA, U.S.A, June, 1995, pp. 210 – 217
    Paper (pdf)
  • A factorization approach to grouping
    P. Perona and W. T. Freeman
    Proceedings, European Conference on Computer Vision, 1998
    Paper (pdf)
  • Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology
    Y. Weiss and W. T. Freeman
    Advances in Neural Information Processing Systems 12, edited by S. A. Solla, T. K. Leen, and K-R Muller, 2000
    Paper (pdf)
  • On the optimality of solutions of the max-product belief propagation algorithm in arbitrary graphs
    Y. Weiss and W. T. Freeman
    IEEE Trans. Information Theory, Special Issue on Codes on Graphs and Iterative Algorithms, 47(2), pp. 723-735, 2001
    Paper (pdf)
  • Learning Low-Level Vision
    W. T. Freeman, E. C. Pasztor, O. T. Carmichael
    International Journal of Computer Vision, 40(1), pp. 25-47, 2000
    Paper (pdf)
  • Learning Joint Statistical Models for Audio-Visual Fusion and Segregation
    J. W. Fisher, T. Darrell, W. T. Freeman and P. Viola
    Advances in Neural Information Processing Systems 13, edited by T. K. Leen, T. G. dietterich, and V. Tresp, pp. 772-778, 2001
  • Generalized Belief Propagation
    J. Yedidia, W. T. Freeman, and Y. Weiss
    Neural Information Processing Systems 13, edited by T. K. Leen, T. G. dietterich, and V. Tresp, pp. 689-695, 2001
    Paper (pdf)
  • Understanding belief propagation and its generalizations
    J. Yedidia, W. T. Freeman and Y. Weiss
    International Joint Conference on Artificial Intelligence (IJCAI 2001), Distinguished Papers Track
    Paper (pdf)
  • Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms
    J. S. Yedidia, W. T. Freeman, and Y. Weiss
    IEEE Transactions on Information Theory, ISSN; 0018-9448, Vol. 51, Issue 7, pp. 2282-2312, July 2005
    Paper (pdf)
  • Nonparametric Belief Propagation and Facial Appearance Estimation
    E. B. Sudderth, A. T. Ihler, W. T. Freeman and A. S. Willsky
    IEEE Computer Vision and Pattern Recognition (CVPR), Madison, WI, June, 2003
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    Project website
  • Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters
    M. F. Tappen and W. T. Freeman
    IEEE Intl. Conference on Computer Vision (ICCV), Nice, France, October, 2003
    Paper (pdf)
  • Efficient multiscale sampling from products of Gaussian mixtures
    A. T. Ihler, E. B. Sudderth, W. T. Freeman, and A. S. Willsky
    Advances in Neural Information Processing Systems 16 (NIPS), Vancouver, BC, MIT Press, 2004
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    Project website
  • Sharing visual features for multiclass and multiview object detection
    A. Torralba, K. P. Murphy, and W. T. Freeman
    IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Washington, DC, 2004; MIT CSAIL technical report
    Paper (pdf)
  • Shared features for multiclass object detection
    A. Torralba, K. Murphy, W. T. Freeman
    Towards Category-Level Object Recognition. Springer Lecture Notes in Computer Science (invited submission). 2005
    Paper (pdf)
  • Sharing visual features for multiclass and multiview object detection
    A. Torralba, K. P. Murphy, and W. T. Freeman
    IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 29, no. 5, pp. 854-869, May, 2007
    Paper (pdf)
  • What makes a good model of natural images?
    Y. Weiss and W. T. Freeman
    IEEE Computer Vision and Pattern Recognition (CVPR) 2007
    Paper (pdf)
  • Signal and Image Processing with Belief Propagation
    E. Sudderth and W. T. Freeman
    DSP Application Column, IEEE Signal Processing Magazine, Mar. 2008
    Paper (pdf)
  • Nonparametric Belief Propagation
    Erik B. Sudderth, Alexander T. Ihler, Michael Isard, William T. Freeman, and Alan S. Willsky
    Communications of the ACM, October, 2010
    Paper (pdf)
  • Exploiting compositionality to explore a large space of model structures
    Roger B. Grosse, Ruslan Salakhutdinov, William T. Freeman, and Joshua B. Tenenbaum
    Conf. on Uncertainty in Artificial Intelligence (UAI), August 2012

    Best Student Paper Prize

    Paper (pdf)