Walter J. Scheirer

walter.scheirer@nd.edu, @wjscheirer

Assistant Professor in the Department of Computer Science and Engineering,
University of Notre Dame

Research Overview

My research is primarily focused around the problem of recognition, including the representations and algorithms supporting solutions to it. I am particularly interested in features and learning-based methods that apply to both vision and language, thus breaking away from the persistent compartmentalization of recognition tasks (something hinted at by David Marr over 30 years ago). This has led to some interesting, and often unconventional approaches that can be applied to a broad set of areas including computer vision, machine learning, human biometrics, and the digital humanities. Specifically, my work is looking at open set recognition, extreme value theory models for visual recognition, biologically-inspired learning algorithms, and stylometry.

Curriculum Vitae

PDF Format

Postdoctoral Fellows

Daniel Moreira

Graduate Students

Sreya Banerjee

Nathaniel Blanchard

Jacob Dumford

Abigail Graese

Samuel Grieggs

Jeffery Kinnison

Derek Prijatelj

Brandon RichardWebster

Bingyu Shen

Rosaura Vidal Mata

William Theisen

Teaching

Fall 2018: CSE 40171 Artificial Intelligence

Spring 2018: CSE 40567/44567/60567 Computer Security

Spring 2017: CSE 40567/60567 Computer Security

Spring 2016: CSE 40567/60567 Computer Security

Fall 2015: CSE 40537/60537 Biometrics

Recent Publications (see all)

"Visual Psychophysics for Making Face Recognition Algorithms More Explainable,"
Brandon RichardWebster, So Yon Kwon, Christopher Clarizio, Samuel E. Anthony,
Walter J. Scheirer,
Proceedings of the European Conference on Computer Vision (ECCV),

September 2018.

[pdf]
[bibtex]
@InProceedings{RichardWebesterFace2018,
author = {Brandon RichardWebster and
So Yon Kwon and
Christopher Clarizio and
Samuel E. Anthony and
Walter J. Scheirer},
title = {Visual Psychophysics for Making Face Recognition Algorithms More Explainable},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2018}
}
"Image Provenance Analysis at Scale,"
Daniel Moreira, Aparna Bharati, Joel Brogan, Allan Pinto, Michael Parowski,
Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter J. Scheirer,
IEEE Transactions on Image Processing (T-IP),

Accepted for Publication July 2018.

[pdf]
[bibtex]
@article{MoreiraProvenance2018,
author = {Daniel Moreira and
Aparna Bharati and
Joel Brogan and
Allan da Silva Pinto and
Michael Parowski and
Kevin W. Bowyer and
Patrick J. Flynn and
Anderson Rocha and
Walter J. Scheirer},
title = {Image Provenance Analysis at Scale},
journal = {IEEE Transactions on Image Processing (T-IP)},
note = {To Appear},
year = {2018}
}
"PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition,"
Brandon RichardWebster, Samuel E. Anthony, Walter J. Scheirer,
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),

Accepted for Publication June 2018.

[pdf]
[bibtex]
@article{RichardWebsterPsyPhy2018,
author = {Brandon RichardWebster and
Samuel E. Anthony and
Walter J. Scheirer},
title = {PsyPhy: {A} Psychophysics Driven Evaluation Framework for Visual Recognition},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)},
note = {To Appear},
year = {2018}
}
"The Limits and Potentials of Deep Learning for Robotics,"
Niko Sunderhauf, Oliver Brock, Walter J. Scheirer, Raia Hadsell, Dieter Fox,
Jurgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford,
Peter Corke,
International Journal of Robotics Research,

April 2018.

[pdf]
[bibtex]
@article{Sunderhauf2018,
author = {Niko Sunderhauf and
Oliver Brock and
Walter J. Scheirer and
Raia Hadsell and
Dieter Fox and
Jurgen Leitner and
Ben Upcroft and
Pieter Abbeel and
Wolfram Burgard and
Michael Milford and
Peter Corke},
title = {The Limits and Potentials of Deep Learning for Robotics},
journal = {International Journal of Robotics Research},
volume = {37},
number = {4-5},
year = {2018}
}
"Using Human Brain Activity to Guide Machine Learning,"
Ruth C. Fong, Walter J. Scheirer, David D. Cox,
Scientific Reports,

March 2018.

[pdf] [supp. material]
[bibtex]
@article{fong2018using,
title={Using human brain activity to guide machine learning},
author={Fong, Ruth C and Scheirer, Walter J and Cox, David D},
journal={Scientific reports},
volume={8},
number={1},
pages={5397},
year={2018},
publisher={Nature Publishing Group}
}
"SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter
Optimization,"
Jeff Kinnison, Nathaniel Kremer-Herman, Douglas Thain, Walter J. Scheirer,
Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV),

March 2018.

[pdf]
[bibtex]
@InProceedings{KinnisonKTS17,
author = {Jeff Kinnison and
Nathaniel Kremer{-}Herman and
Douglas Thain and
Walter J. Scheirer},
title = {{SHADHO:} Massively Scalable Hardware-Aware Distributed Hyperparameter
Optimization},
booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
year = {2018}
}
"UG^2: a Video Benchmark for Assessing the Impact of Image Restoration
and Enhancement on Automatic Visual Recognition,"
Rosaura Vidal Mata, Sreya Banerjee, Klemen Grm, Vitomir Struc, Walter J. Scheirer,
Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV),

March 2018.

[pdf] [dataset] [workshop]
[bibtex]
@InProceedings{vidal2018ug,
title={UG$^{2}$: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition},
author={Vidal, Rosaura G and Banerjee, Sreya and Grm, Klemen and Struc, Vitomir and Scheirer, Walter J},
booktitle={IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2018}
}
"The Extreme Value Machine,"
Ethan Rudd, Lalit P. Jain, Walter J. Scheirer, Terrance Boult,
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),

March 2018.

[pdf] [supp. material] [code]
[bibtex]
@article{Rudd_2018_TPAMI,
author = {Ethan Rudd and Lalit P. Jain and Walter J. Scheirer and Terrance Boult},
title = {The Extreme Value Machine},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)},
volume = {40},
number = {3},
month = {March},
year = {2018}
}

New Research in arXiv / bioRxiv

"A Neurobiological Cross-domain Evaluation Metric for Predictive Coding Networks,"
Nathaniel Blanchard, Jeffery Kinnison, Brandon RichardWebster, Pouya Bashivan,
Walter J. Scheirer,
May 2018.

[pdf]
[bibtex]
@article{DBLP:journals/corr/Blanchard18,
author = {Nathaniel Blanchard and
Jeffery Kinnison and
Brandon RichardWebster and
Pouya Bashivan and
Walter J. Scheirer},
title = {A Neurobiological Cross-domain Evaluation Metric for Predictive Coding Networks},
journal = {CoRR},
volume = {abs/1805.10726},
year = {2018},
url = {https://arxiv.org/abs/1805.10726}
}
"Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal
Circuit Tracing,"
Ali Shahbazi, Jeffery Kinnison, Rafael Vescovi, Ming Du, Robert Hill, Maximilian Joesch,
Marc Takeno, Hongkui Zeng, Nuno da Costa, Jaime Grutzendler, Narayanan Kasthuri,
Walter J. Scheirer,
March 2018.

[pdf]
[bibtex]
@article {Shahbazi278515,
author = {Shahbazi, Ali and
Kinnison, Jeffery and
Vescovi, Rafael and
Du, Ming and
Hill, Robert and
J{\"o}sch, Maximilian and
Takeno, Marc and
Zeng, Hongkui and
da Costa, Nuno Ma{\c c}arico and
Grutzendler, Jaime and
Kasthuri, Narayanan and
Scheirer, Walter J.},
title = {Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing},
year = {2018},
doi = {10.1101/278515},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2018/07/06/278515},
eprint = {https://www.biorxiv.org/content/early/2018/07/06/278515.full.pdf},
journal = {bioRxiv}
}

Recent Projects (see all)

Hardware-Aware Distributed Hyperparameter Optimization

Spring 2017 - Present

Intelligent mapping of hardware resources to model search tasks

Keywords: machine learning, hyperparameters, optimization, distributed computing, connectomics

Image Restoration and Enhancement for Visual Recognition

Fall 2016 - Present

Work to bridge the gap between computational photography and visual recognition

Keywords: deblurring, denoising, super-resolution, deep learning, object recognition, video

Tools for Neuroscience

Fall 2015 - Present

Techniques to help neuroscientists understand neuroanatomy and function

Keywords: connectomics, electron microscopy, two-photon imaging, x-ray tomography, 3D reconstruction

Brain-informed Machine Learning

Fall 2014 - Present

Applying measurements of neural activity to regularize machine learning models

Keywords: fMRI, psychophysics, classification, deep learning, neural-weights, object recognition

Visual Place Recognition

Spring 2013 - Present

Condition invariant visual place recognition for robot navigation inspired by vision science

Keywords: robotics, navigation, place recognition, whole image matching, patch matching, calibration

Good Recognition is Non-metric

Fall 2012 - Spring 2014

A new look at a fundamental question in computer vision: is recognition metric?

Keywords: machine learning, metric learning, recognition, face recognition, object recognition

Psychophysics for Computer Vision

Fall 2012 - Present

Measuring exemplar-by-exemplar difﬁculty and the pattern of errors of humans for supervised learning

Keywords: machine learning, psychology, citizen science, psychophysics, face detection, attributes

Open Set Recognition

Spring 2011 - Present

Theory and algorithms that address the difficult problem of training without complete class knowledge

Keywords: machine learning, object recognition, face recognition, support vector machines, 1-vs-set machine

Extreme Value Theory for Visual Recognition

Spring 2008 - Present

The theory and practice of recognition score analysis for prediction and fusion

Keywords: calibration, meta-recognition, score analysis, object recognition, face recognition, attributes

Language & Literature

Spring 2009 - Present

Machine learning and related statistical methods to improve the process by which intertextuality is studied

Keywords: computational linguistics, intertextuality, sound, stylistics, classics, authorship attribution

Digital Image and Video Forensics

Spring 2008 - Present

Approaches to steganalysis, forgery detection and sensor fingerprinting

Keywords: forensics, forgery detection, data hiding, source identification, sensor fingerprinting

Recent Talks (see all)

Invited talk at the CVPR 2018 Workshop "Real-World Challenges and New Benchmarks for Deep Learning in Robotic Vision":
A Psychophysics Driven Evaluation Framework for Visual Recognition
Invited talk at Amazon: Using Human Behavior and Brain Activity to Guide Machine Learning
Invited talk at Cultural Analytics 2017: Quantitative Intertextuality: Analyzing the Markers of Information Reuse
Invited talk at Argonne National Laboratory: Scalable Strategies for Image Analysis in Neuroscience
CVPR 2016 Tutorial: Statistical Methods for Open Set Recognition
Invited talk at the RSS 2016 Workshop "Are the Sceptics Right? Limits and Potentials of Deep Learning in Robotics":
The Impact of the Open Set Recognition Problem on Deep Learning (Video)
WIFS 2015 Tutorial: The Open Set Recognition Problem in Information Forensics and Security

Activities

The Interactive and Adaptive Learning in an Open World workshop will be co-located with ECCV 2018
CVPR 2019 will be held in Long Beach, California, June 15th - 21st
The UG^2 Challenge prize challenge workshop on image restoration and enhancement was held at CVPR 2018
Join the PAMI-TC (now on LinkedIn) and the Computer Vision Foundation