Report: Second GroupSight Workshop on Human Computation for Image and Video Analysis

What would be possible if we could accelerate the analysis of images and videos, especially at scale? This question is generating widespread interest across research communities as diverse as computer vision, human computer interaction, computer graphics, and multimedia.

The second Workshop on Human Computation for Image and Video Analysis (GroupSight) took place in Quebec City, Canada on October 24, 2017, as part of HCOMP 2017. The goal of the workshop was to promote greater interaction between this diversity of researchers and practitioners who examine how to mix human and computer efforts to convert visual data into discoveries and innovations that benefit society at large.

This was the second edition of the GroupSight workshop to be held at HCOMP. It was also the first time the workshop and conference were co-located with UIST. A website and blog post on the first edition of GroupSight are also available.

The workshop featured two keynote speakers in HCI doing research on crowdsourced image analysis. Meredith Ringel Morris (Microsoft Research) presented work on combining human and machine intelligence to describe images to people with visual impairments (slides). Walter Lasecki (University of Michigan) discussed projects using real-time crowdsourcing to rapidly and scalably generate training data for computer vision systems.

Participants also presented papers along three emergent themes:

Leveraging the visual capabilities of crowd workers:

  • Abdullah Alshaibani and colleagues at Purdue University presented InFocus, a system enabling untrusted workers to redact potentially sensitive content from imagery. (Best Paper Award)
  • Kyung Je Jo and colleagues at KAIST presented Exprgram (paper, video). This paper introduced a crowd workflow that supports language learning while annotating and searching videos. (Best Paper Runner-Up Award)
  • GroundTruth (paper, video), a system by Rachel Kohler and colleagues at Virginia Tech, combined expert investigators and novice crowds to identify the precise geographic location where images and videos were created.

Kurt Luther hands the best paper award to Alex Quinn.

Creating synergies between crowdsourced human visual analysis and computer vision:

  • Steven Gutstein and colleagues from the U.S. Army Research Laboratory presented a system that integrated a brain-computer interface with computer vision techniques 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.

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.
  • Anuparna Banerjee and colleagues at UT Austin presented an analysis of ways that crowd workers disagree in visual question answering tasks.

The workshop also had break-out groups where 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 (to computer vision and in general), and crowdsourcing ethics.

A group of researchers talking and seated around a poster board covered in sticky notes.

For more, including keynote slides and papers, check out the workshop website:

Danna Gurari, UT Austin
Kurt Luther, Virginia Tech
Genevieve Patterson, Brown University and Microsoft Research New England
Steve Branson, Caltech
James Hays, Georgia Tech
Pietro Perona, Caltech
Serge Belongie, Cornell Tech


Crowdsourcing the Location of Photos and Videos

How can crowdsourcing help debunk fake news and prevent the spread of misinformation? In this paper, we explore how crowds can help expert investigators verify the claims around visual evidence they encounter during their work.

A key step in image verification is geolocation, the process of identifying the precise geographic location where a photo or video was created. Geotags or other metadata can be forged or missing, so expert investigators will often try to manually locate the image using visual clues, such as road signs, business names, logos, distinctive architecture or landmarks, vehicles, and terrain and vegetation.

However, sometimes there are not enough clues to make a definitive geolocation. In these cases, the expert will often draw an aerial diagram, such as the one shown below, and then try to find a match by analyzing miles of satellite imagery.

An aerial diagram of a ground-level photo, and the corresponding satellite imagery of that location.

Source: Bellingcat

This can be a very tedious and overwhelming task – essentially finding a needle in a haystack. We proposed that crowdsourcing might help, because crowds have good visual recognition skills and can scale up, and satellite image analysis can be highly parallelized. However, novice crowds would have trouble translating the ground-level photo or video into an aerial diagram, a process that experts told us requires lots of practice.

Our approach to solving this problem was right in front of us: what if crowds also use the expert’s aerial diagram? The expert was going to make the diagram anyway, so it’s no extra work for them, but it would allow novice crowds to bridge the gap between ground-level photo and satellite imagery.

To evaluate this approach, we conducted two experiments. The first experiment looked at how the level of detail in the aerial diagram affected the crowd’s geolocation performance. We found that in only ten minutes, crowds could consistently narrow down the search area by 40-60%, while missing the correct location only 2-8% of the time, on average.


In our second experiment, we looked at whether to show crowds the ground-level photo, the aerial diagram, or both. The results confirmed our intuition: the aerial diagram was best. When we gave crowds just the ground-level photo, they missed the correct location 22% of the time – not bad, but probably not good enough to be useful, either. On the other hand, when we gave crowds the aerial diagram, they missed the correct location only 2% of the time – a game-changer.

Bar chart showing the diagram condition performed significantly better than the ground photo condition.

For next steps, we are building a system called GroundTruth (video) that brings together experts and crowds to support image geolocation. We’re also interested in ways to synthesize our crowdsourcing results with recent advances in image geolocation from the computer vision research community.

For more, see our full paper, Supporting Image Geolocation with Diagramming and Crowdsourcing, which received the Notable Paper Award at HCOMP 2017.

Rachel Kohler, Virginia Tech
John Purviance, Virginia Tech
Kurt Luther, Virginia Tech

Call for Participation: GroupSight 2017

The Second Workshop on Human Computation for Image and Video Analysis (GroupSight) is to be held on October 24, 2017 at AAAI HCOMP 2017 at Québec City, Canada. This promises an exciting mix of people and papers at the intersection of HCI, crowdsourcing, and computer vision.

The aim of this workshop is to promote greater interaction between the diversity of researchers and practitioners who examine how to mix human and computer efforts to convert visual data into discoveries and innovations that benefit society at large. It will foster in-depth discussion of technical and application issues for how to engage humans with computers to optimize cost/quality trade-offs. It will also serve as an introduction to researchers and students curious about this important, emerging field at the intersection of crowdsourced human computation and image/video analysis.

Topics of Interest

Crowdsourcing image and video annotations (e.g., labeling methods, quality control, etc.)
Humans in the loop for visual tasks (e.g., recognition, segmentation, tracking, counting, etc.)
Richer modalities of communication between humans and visual information (e.g., language, 3D pose, attributes, etc.)
Semi-automated computer vision algorithms
Active visual learning
Studies of crowdsourced image/video analysis in the wild

Submission Details

Submissions are requested in the following two categories: Original Work (not published elsewhere) and Demo (describing new systems, architectures, interaction techniques, etc.). Papers should be submitted as 4-page extended abstracts (including references) using the provided author kit. Demos should also include a URL to a video (max 6 min). Multiple submissions are not allowed. Reviewing will be double-blind.
Previously published work from a recent conference or journal can be considered but the authors should submit an unrevised copy of their published work. Reviewing will be single-blind. Email submissions to

Important Dates

August 14August 23, 2017: Deadline for paper submission (5:59 pm EDT)
August 25, 2017: Notification of decision
October 24, 2017: Workshop (full-day)