Machine Learning

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)  :  Project Repo

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)

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)

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)  :  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)  :  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)

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)  :  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)  :  Project Page

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)  :  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)

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)

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)

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)

Signal and Image Processing with Belief Propagation

E. Sudderth and W. T. Freeman
DSP Application Column, IEEE Signal Processing Magazine, Mar. 2008
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)

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)

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)

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)

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
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)

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)

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
Project website

Shape Recipes: Scene Representations that Refer to the Image

William T. Freeman, Antonio Torralba
Neural Information Processing Systems (NIPS) 2002
Paper (pdf)

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)

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

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)

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)

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)

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)

A factorization approach to grouping

P. Perona and W. T. Freeman
Proceedings, European Conference on Computer Vision, 1998
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)