The WashU Vision & Learning Group is part of the Computer Science & Engineering department at Washington University in St. Louis. We work on problems in computer vision, computational photography, and machine learning—dealing with the design of accurate and efficient algorithms for visual inference, and of new kinds of high-capability sensors and cameras. Our research is built on analyzing the physics of image formation, and learning and exploiting the statistical structure that exists in natural images and scenes.
We are looking to recruit talented PhD students. Please apply directly to the CSE PhD program.
what's new ?
New papers posted to arXiv on internal recurrence-based image modeling, and learning privacy-preserving image encodings.
Zhihao Xia joins our group as a PhD student. Zhihao completed his undergrad in CS from USTC, with stints as a visiting student researcher at NUS and NII Japan.
research
Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising
arXiv 2018
Zhihao Xia, Ayan Chakrabarti
Paper: arXiv
Learning Privacy Preserving Encodings through Adversarial Training
WACV 2019
Francesco Pittaluga, Sanjeev J. Koppal, Ayan Chakrabarti
Paper: arXiv
Stabilizing GAN Training with Multiple Random Projections
arXiv 2017
Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti
Paper: arXiv URL: Project website
Jointly Optimizing Placement and Inference for Beacon-based Localization
IROS 2017
Charles Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter
Paper: arXiv URL: Project website
Examining the Impact of Blur on Recognition by Convolutional Networks
arXiv 2017
Igor Vasiljevic, Ayan Chakrabarti, Gregory Shakhnarovich
Paper: arXiv
Learning Sensor Multiplexing Design through Back-propagation
NIPS 2016
Ayan Chakrabarti
Paper: arXiv URL: Project website
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
NIPS 2016
Ayan Chakrabarti, Jingyu Shao, Gregory Shakhnarovich
Paper: arXiv URL: Project website
Single-image RGB Photometric Stereo With Spatially-varying Albedo
3DV 2016 oral
Ayan Chakrabarti, Kalyan Sunkavalli
Paper: arXiv URL: Project website
A Neural Approach to Blind Motion Deblurring
ECCV 2016
Ayan Chakrabarti
Paper: arXiv URL: Project website
Color Constancy by Learning to Predict Chromaticity from Luminance
NIPS 2015 spotlight
Ayan Chakrabarti
Paper: arXiv (w/ supp. section) / NIPS URL: Project website
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
CVPR 2015
Ayan Chakrabarti, Ying Xiong, Steven J. Gortler, Todd Zickler
Paper: CV-F / arXiv URL: Project website
From Shading to Local Shape
PAMI 2015
Ying Xiong, Ayan Chakrabarti, Ronen Basri, Steven J. Gortler, David W. Jacobs, Todd Zickler
Paper: IEEE Xplore / arXiv URL: Project website
Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images
PAMI 2014
Ayan Chakrabarti, Ying Xiong, Baochen Sun, Trevor Darrell, Daniel Scharstein, Todd Zickler, Kate Saenko
Paper: IEEE Xplore / arXiv URL: Project website
Rethinking Color Cameras
ICCP 2014
Ayan Chakrabarti, William T. Freeman, Todd Zickler
Paper: IEEE Xplore URL: Project website
Depth and Deblurring from a Spectrally-varying Depth-of-Field
ECCV 2012
Ayan Chakrabarti, Todd Zickler
Paper: PDF URL: Project website, Spotlight Video
Color Constancy with Spatio-Spectral Statistics
PAMI 2012
Ayan Chakrabarti, Keigo Hirakawa, Todd Zickler
Paper: IEEE Xplore URL: Project website
Statistics of Real-World Hyperspectral Images
CVPR 2011
Ayan Chakrabarti, Todd Zickler
Paper: IEEE Xplore URL: Project website
Learning Object Color Models from Multi-view Constraints
CVPR 2011
Trevor Owens, Kate Saenko, Ayan Chakrabarti, Ying Xiong, Todd Zickler, Trevor Darrell
Paper: IEEE Xplore
Analyzing Spatially-varying Blur
CVPR 2010
Ayan Chakrabarti, Todd Zickler, William T. Freeman
Paper: IEEE Xplore URL: Project website
An Empirical Camera Model for Internet Color Vision
BMVC 2009
Ayan Chakrabarti, Daniel Scharstein, Todd Zickler
Paper: PDF URL: Project website
Color Constancy Beyond Bags of Pixels
CVPR 2008
Ayan Chakrabarti, Keigo Hirakawa, Todd Zickler
Paper: IEEE Xplore
Effective Separation of Sparse and Non-sparse Image Features for Denoising
ICASSP 2008
Ayan Chakrabarti, Keigo Hirakawa
Paper: IEEE Xplore
Super-Resolution of Face Images Using Kernel PCA-Based Prior
IEEE Transactions on Multimedia 2007
Ayan Chakrabarti, A.N. Rajagopalan, Rama Chellappa
Paper: IEEE Xplore
Visual Inference with Statistical Models for Color and Texture
Ph.D. Dissertation, School of Engineering and Applied Sciences, Harvard University, 2011
Ayan Chakrabarti
PDF
people
Former Members & Visitors
Contact
Prof. Ayan Chakrabarti
Computer Science & Engineering
Washington University in St. Louis
1 Brookings Dr. CB #1045
St. Louis, MO 63130