PeopleofSuperintelligence

Researcher

Mark Everingham

Mark Everingham was a computer-vision researcher whose work connected video recognition, human pose estimation and benchmark evaluation. In 2005, he and Andrew Zisserman published a method for identifying people in consumer video. He led PASCAL VOC's data and evaluation work, then developed methods for sign-language recognition and human pose estimation from weak supervision. At Leeds, he organized BMVC in 2008 and supervised a 2011 PhD project on sign and action recognition from broadcasts. He died in 2012, and the VOC workshop at ECCV in 2012 was dedicated to his memory.

Timeline

  1. 2005

    Recognizing people in video

    In 2005, he and Andrew Zisserman publish a method to detect and identify people in unconstrained consumer video from a small set of labelled faces.

  2. 2005–2012

    The PASCAL VOC challenge

    From its start in 2005 through the 2012 challenge, he leads PASCAL VOC's data organization, annotation, evaluation software, quality control and workshops. The challenge provides a benchmark for object recognition.

  3. 2008

    Organizing BMVC in Leeds

    In 2008, he is the main organizer of BMVC, held in Leeds.

  4. 2009

    Sign language from television

    In 2009, with Patrick Buehler and Andrew Zisserman, he develops a method that learns British Sign Language signs from television by using subtitles as weak supervision.

  5. August 2010

    Pose estimation from weak labels

    In August 2010, he serves as the University of Leeds contact for an EPSRC project on articulated human pose estimation from weak supervision; its aims include a large dataset of approximately annotated consumer images.

  6. June 2011

    Supervising broadcast-video research

    In June 2011, he supervises a Leeds PhD studentship in video recognition. The project develops visual descriptors and learning algorithms for sign language and action recognition in broadcast video, using subtitles as weak supervision.

  7. 2012

    A final VOC workshop

    He dies in 2012, and the VOC workshop at ECCV 2012 is dedicated to his memory.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of Leeds. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of Leeds. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Leeds. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Leeds. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Identifying Individuals in Video by Combining Generative and Discriminative Head Models robots.ox.ac.uk. Checked 21 October 2026.
  2. The British Machine Vision Association : Mark Everingham bmva.org. Checked 21 October 2026.
  3. Learning Sign Language by Watching TV (using Weakly Aligned Subtitles) robots.ox.ac.uk. Checked 21 October 2026.
  4. Research Fellow in Computer Vision and Machine Learning k4all.org. Checked 21 October 2026.
  5. PhD Studentship: Learning to Recognise Dynamic Visual Content from Broadcast Footage cvpr2011.thecvf.com. Checked 21 October 2026.
  6. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) robots.ox.ac.uk. Checked 21 October 2026.