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

Sophia Abraham

Sreya Banerjee

Zachariah Carmichael

Samuel Grieggs

Jin Huang

Jeffery Kinnison

Derek Prijatelj

Brandon RichardWebster

Bingyu Shen

Rosaura Vidal Mata (Co-Advised with Kevin Bowyer)

William Theisen

Teaching

Fall 2019: CSE 40171 Artificial Intelligence

Spring 2019: CSE 40567/60567 Computer Security

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)

"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),

September 2019.

[pdf]
[bibtex]
@article{RichardWebsterPsyPhy2019,
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)},
volume = {41},
number = {9},
month = {September},
year = {2019}
}
"A Neurobiological Evaluation Metric for Neural Network Model Search,"
Nathaniel Blanchard, Jeffery Kinnison, Brandon RichardWebster, Pouya Bashivan,
Walter J. Scheirer,
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),

June 2019.

[pdf]
[bibtex]
@InProceedings{Blanchard19,
author = {Nathaniel Blanchard and
Jeffery Kinnison and
Brandon RichardWebster and
Pouya Bashivan and
Walter J. Scheirer},
title = {A Neurobiological Evaluation Metric for Neural Network Model Search},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019},
}
"An Extreme Value Theory Model of Cross-Modal Sensory Information Integration in
Modulation of Vertebrate Visual System Functions,"
Sreya Banerjee, Walter J. Scheirer, Lei Li,
Frontiers in Computational Neuroscience,

February 2019.

[pdf]
[bibtex]
@article{Banerjee423038,
author = {Sreya Banerjee and
Walter Scheirer and
Lei Li},
title = {An Extreme Value Theory Model of Cross-Modal Sensory Information Integration in
Modulation of Vertebrate Visual System Functions},
journal = {Frontiers in Computational Neuroscience},
volume = {13},
month = {February},
year = {2019},
pages = {3}
}
"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),

December 2018.

[pdf] [dataset] [code]
[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)},
volume = {27},
number = {12},
year = {2018}
}
"Practical Text Phylogeny for Real-World Settings,"
Bingyu Shen, Christopher W. Forstall, Anderson Rocha Walter J. Scheirer,
IEEE Access,

December 2018.

[pdf] [code]
[bibtex]
@article{ShenAccess2018,
author = {Bingyu Shen and
Christopher W. Forstall and
Anderson Rocha and
Walter J. Scheirer},
title = {Practical Text Phylogeny for Real-World Settings},
journal = {IEEE Access},
volume = {6},
number = {1},
year = {2018}
}
"Flexible Learning-Free Segmentation and Reconstruction for 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,
Scientific Reports,

September 2018.

[pdf] [code]
[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 Neuronal Circuit Tracing},
journal = {Scientific Reports}
volume = {8},
number = {14247},
year = {2018},
}
"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}
}
"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}
}

New Research in arXiv / bioRxiv

"Measuring Human Perception to Improve Handwritten Document Transcription,"
Samuel Grieggs, Bingyu Shen, Pei Li, Cana Short, Jiaqi Ma, Mihow McKenny,
Melody Wauke, Brian Price, Walter J. Scheirer,
April 2019.

[pdf]
[bibtex]
@article{DBLP:journals/corr/abs-1904-03734,
author = {Samuel Grieggs and
Bingyu Shen and
Pei Li and
Cana Short and
Jiaqi Ma and
Mihow McKenny and
Melody Wauke and
Brian Price and
Walter J. Scheirer},
title = {Measuring Human Perception to Improve Handwritten Document Transcription},
journal = {CoRR},
volume = {abs/1904.03734},
year = {2019},
url = {http://arxiv.org/abs/1904.03734},
archivePrefix = {arXiv},
eprint = {1904.03734},
}
"Needle in a Haystack: A Framework for Seeking Small Objects in Big Datasets,"
Joel Brogan, Aparna Bharati, Daniel Moreira, Kevin Bowyer, Patrick Flynn,
Anderson Rocha, Walter J. Scheirer,
March 2019.

[pdf]
[bibtex]
@article{DBLP:journals/corr/abs-1903-10019,
author = {Joel Brogan and
Aparna Bharati and
Daniel Moreira and
Kevin W. Bowyer and
Patrick J. Flynn and
Anderson Rocha and
Walter J. Scheirer},
title = {Needle in a Haystack: {A} Framework for Seeking Small Objects in Big Datasets},
journal = {CoRR},
volume = {abs/1903.10019},
year = {2019},
url = {http://arxiv.org/abs/1903.10019},
archivePrefix = {arXiv},
eprint = {1903.10019},
timestamp = {Mon, 01 Apr 2019 14:07:37 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-10019},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"Bridging the Gap Between Computational Photography and Visual Recognition,"
Rosaura G. VidalMata, Sreya Banerjee, Brandon RichardWebster, Michael Albright,
Pedro Davalos, Scott McCloskey, Ben Miller, Asong Tambo, Sushobhan Ghosh,
Sudarshan Nagesh, Ye Yuan, Yueyu Hu, Junru Wu, Wenhan Yang, Xiaoshuai Zhang,
Jiaying Liu, Zhangyang Wang, Hwann-Tzong Chen, Tzu-Wei Huang, Wen-Chi Chin,
Yi-Chun Li, Mahmoud Lababidi, Charles Otto, Walter J. Scheirer,
January 2019.

[pdf]
[bibtex]
@misc{vidalmata2019bridging,
title = {Bridging the Gap Between Computational Photography and Visual Recognition},
author = {Rosaura G. VidalMata and
Sreya Banerjee and
Brandon RichardWebster and
Michael Albright and
Pedro Davalos and
Scott McCloskey and
Ben Miller and
Asong Tambo and
Sushobhan Ghosh and
Sudarshan Nagesh and
Ye Yuan and
Yueyu Hu and
Junru Wu and
Wenhan Yang and
Xiaoshuai Zhang and
Jiaying Liu and
Zhangyang Wang and
Hwann-Tzong Chen and
Tzu-Wei Huang and
Wen-Chi Chin and
Yi-Chun Li and
Mahmoud Lababidi and
Charles Otto and
Walter J. Scheirer},
year = {2019},
eprint = {1901.09482},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
"Backdooring Convolutional Neural Networks via Targeted Weight Perturbations,"
Jacob Dumford, Walter J. Scheirer,
December 2018.

[pdf]
[bibtex]
@article{DBLP:journals/corr/Dumford18,
author = {Jacob Dumford and
Walter J. Scheirer},
title = {Backdooring Convolutional Neural Networks via Targeted Weight Perturbations},
journal = {CoRR},
volume = {abs/1812.03128},
year = {2018},
url = {https://arxiv.org/abs/1812.03128}
}
"On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs,"
Sandipan Banerjee, Walter J. Scheirer, Kevin Bowyer, Patrick Flynn,
November 2018.

[pdf]
[bibtex]
@article{DBLP:journals/corr/abs-1811-07104,
author = {Sandipan Banerjee and
Walter J. Scheirer and
Kevin W. Bowyer and
Patrick J. Flynn},
title = {On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs},
journal = {CoRR},
volume = {abs/1811.07104},
year = {2018},
url = {http://arxiv.org/abs/1811.07104},
archivePrefix = {arXiv},
eprint = {1811.07104},
timestamp = {Sun, 25 Nov 2018 18:57:12 +0100},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1811-07104},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

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

Computational Models of Zebrafish Sensory Integration

Fall 2016 - Present

Modeling neural circuits to explain fish behavior and apply to generalized recognition problems in AI

Keywords: computational neuroscience, zebrafish, sensory integration, data fusion, extreme value theory

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

Face Synthesis

Fall 2015 - Present

Fast and accurate photo-quality face synthesis for use in face recognition work

Keywords: face synthesis, face recognition, face analysis, computer graphics, data augmentation

Learning-free Image Segmentation

Fall 2015 - Present

Image segmentation for settings where machine learning-based approaches are slow and do not generalize

Keywords: image filtering, volumetric segmentation, connectomics, medical imaging, iris recognition

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)

Talk at the DEF CON 27 AI Village: Backdooring Convolutional Neural Networks with Targeted Weight Perturbations
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)

Activities

CVPR 2020 will be held in Seattle, Washington, June 14th - 19th
WACV 2020 will be held in Snowmass Village, CO, March 2nd - 5th
CVPR 2019 was held in Long Beach, California, June 15th - 21st
The UG^2+ Challenge prize challenge workshop on image restoration and enhancement was held at CVPR 2019
The Interactive and Adaptive Learning in an Open World workshop was co-located with ECCV 2018
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