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Dive into the research topics where Michael J. Wilber is active.

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Featured researches published by Michael J. Wilber.


Pattern Recognition | 2014

Good recognition is non-metric

Walter J. Scheirer; Michael J. Wilber; Michael Eckmann; Terrance E. Boult

Abstract Recognition is the fundamental task of visual cognition, yet how to formalize the general recognition problem for computer vision remains an open issue. The problem is sometimes reduced to the simplest case of recognizing matching pairs, often structured to allow for metric constraints. However, visual recognition is broader than just pair-matching: what we learn and how we learn it has important implications for effective algorithms. In this review paper, we reconsider the assumption of recognition as a pair-matching test, and introduce a new formal definition that captures the broader context of the problem. Through a meta-analysis and an experimental assessment of the top algorithms on popular data sets, we gain a sense of how often metric properties are violated by recognition algorithms. By studying these violations, useful insights come to light: we make the case for local distances and systems that leverage outside information to solve the general recognition problem.


workshop on applications of computer vision | 2013

Animal recognition in the Mojave Desert: Vision tools for field biologists

Michael J. Wilber; Walter J. Scheirer; Phil Leitner; Brian Heflin; James Zott; Daniel Reinke; David K. Delaney; Terrance E. Boult

The outreach of computer vision to non-traditional areas has enormous potential to enable new ways of solving real world problems. One such problem is how to incorporate technology in the effort to protect endangered and threatened species in the wild. This paper presents a snapshot of our interdisciplinary teams ongoing work in the Mojave Desert to build vision tools for field biologists to study the currently threatened Desert Tortoise and Mohave Ground Squirrel. Animal population studies in natural habitats present new recognition challenges for computer vision, where open set testing and access to just limited computing resources lead us to algorithms that diverge from common practices. We introduce a novel algorithm for animal classification that addresses the open set nature of this problem and is suitable for implementation on a smartphone. Further, we look at a simple model for object recognition applied to the problem of individual species identification. A thorough experimental analysis is provided for real field data collected in the Mojave desert.


workshop on applications of computer vision | 2014

Exemplar codes for facial attributes and tattoo recognition

Michael J. Wilber; Ethan M. Rudd; Brian Heflin; Yui-Man Lui; Terrance E. Boult

When implementing real-world computer vision systems, researchers can use mid-level representations as a tool to adjust the trade-off between accuracy and efficiency. Unfortunately, existing mid-level representations that improve accuracy tend to decrease efficiency, or are specifically tailored to work well within one pipeline or vision problem at the exclusion of others. We introduce a novel, efficient mid-level representation that improves classification efficiency without sacrificing accuracy. Our Exemplar Codes are based on linear classifiers and probability normalization from extreme value theory. We apply Exemplar Codes to two problems: facial attribute extraction and tattoo classification. In these settings, our Exemplar Codes are competitive with the state of the art and offer efficiency benefits, making it possible to achieve high accuracy even on commodity hardware with a low computational budget.


user interface software and technology | 2017

Crowd Research: Open and Scalable University Laboratories

Rajan Vaish; Snehalkumar (Neil) S. Gaikwad; Geza Kovacs; Andreas Veit; Ranjay Krishna; Imanol Arrieta Ibarra; Camelia Simoiu; Michael J. Wilber; Serge J. Belongie; Sharad Goel; James Davis; Michael S. Bernstein

Research experiences today are limited to a privileged few at select universities. Providing open access to research experiences would enable global upward mobility and increased diversity in the scientific workforce. How can we coordinate a crowd of diverse volunteers on open-ended research? How could a PI have enough visibility into each persons contributions to recommend them for further study? We present Crowd Research, a crowdsourcing technique that coordinates open-ended research through an iterative cycle of open contribution, synchronous collaboration, and peer assessment. To aid upward mobility and recognize contributions in publications, we introduce a decentralized credit system: participants allocate credits to each other, which a graph centrality algorithm translates into a collectively-created author order. Over 1,500 people from 62 countries have participated, 74% from institutions with low access to research. Over two years and three projects, this crowd has produced articles at top-tier Computer Science venues, and participants have gone on to leading graduate programs.


neural information processing systems | 2016

Residual networks behave like ensembles of relatively shallow networks

Andreas Veit; Michael J. Wilber; Serge J. Belongie


arXiv: Computer Vision and Pattern Recognition | 2016

Residual Networks are Exponential Ensembles of Relatively Shallow Networks.

Andreas Veit; Michael J. Wilber; Serge J. Belongie


national conference on artificial intelligence | 2014

Cost-Effective HITs for Relative Similarity Comparisons

Michael J. Wilber; Iljung S. Kwak; Serge J. Belongie


international conference on computer vision | 2015

Learning Concept Embeddings with Combined Human-Machine Expertise

Michael J. Wilber; Iljung S. Kwak; David J. Kriegman; Serge J. Belongie


workshop on applications of computer vision | 2016

Can we still avoid automatic face detection

Michael J. Wilber; Vitaly Shmatikov; Serge J. Belongie


international conference on computer vision | 2017

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

Michael J. Wilber; Chen Fang; Hailin Jin; Aaron Hertzmann; John P. Collomosse; Serge J. Belongie

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Terrance E. Boult

University of Colorado Colorado Springs

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Brian Heflin

University of Colorado Colorado Springs

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Iljung S. Kwak

University of California

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James Davis

University of California

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