presented work on combining human and machine
intelligence to describe images to people with visual
impairments. Walter Lasecki (University of Michigan) discussed projects using real-time crowdsourcing to rapidly and scalably generate training data for
computer vision systems.
One major theme of the workshop papers was
building systems that leverage the visual capabilities
of crowdsourced human workers. Abdullah
Alshaibani and colleagues at Purdue University presented InFocus, the workshop’s Best Paper award
winner. This system enables untrusted workers to
redact potentially sensitive content from imagery.
Kyung Je Jo and colleagues at KAIST presented
Exprgram, the workshop’s Best Paper Runner-Up
award winner. This paper introduced a crowd work-flow that supports language learning while annotating and searching videos. Ground Truth, a system by
Rachel Kohler and colleagues at Virginia Tech, combines expert investigators and novice crowds to identify the precise geographic location where images
and videos were created.
Another major theme of the papers was creating
synergies between crowdsourced human visual
analysis and computer vision techniques. Steven
Gutstein and colleagues from the US Army Research
Laboratory presented a system that integrates a
brain-computer interface with computer vision tech-
niques to support rapid triage of images. Divya
Ramesh and colleagues from CloudSight presented
an approach for real-time captioning of images by
combining crowdsourcing and computer vision.
A third theme of the workshop papers was improving methods for aggregating results from crowdsourced image analysis. Jean Song and colleagues at
the University of Michigan presented research showing that tool diversity can improve aggregate crowd
performance on image segmentation tasks. Anupar-na Banerjee and colleagues at the University of Texas,
Austin presented an analysis of ways that crowd
workers disagree in visual question-answering tasks.
The workshop also featured a poster session and
break-out groups. Participants used a bottom-up
approach to identify topical clusters of common
research interests and open problems. These clusters
included real-time crowdsourcing, worker abilities,
applications (both to computer vision and in general), and crowdsourcing ethics.
Danna Gurari (UT Austin), Kurt Luther (Virginia
Tech), Genevieve Patterson (Brown University and
Microsoft Research New England), and Steve Branson
(Caltech) served as cochairs of this workshop. The
steering committee comprised James Hays (Georgia
Tech), Pietro Perona (Caltech), and Serge Belongie
(Cornell Tech). Some of the papers are available on
the workshop website, groupsight.github.io.
Kurt Luther is an assistant professor in the Department of
Computer Science at Virginia Tech.
The Workshop Was Held in Quebec City, Quebec, Canada.