PAMI Mark Everingham Prize

This Prize is to commemorate Mark Everingham and to encourage others to follow in his footsteps by acting to further progress in the computer vision community as a whole. An appreciation of Mark Everingham’s contributions is at The prize shall be given to a researcher, or a team of researchers, who have made a selfless contribution of significant benefit to other members of the computer vision community. The award is given out by the IEEE Pattern Analysis and Machine Intelligence (PAMI) Technical Committee. Candidates are nominated by the community in a window preceding ECCV or ICCV determined by the TCPAMI chair.

If you have questions about submitting a nomination, contact the TCPAMI chair. Winners are decided by a committee appointed by the TCPAMI Awards Committee.

Click here for the wikipedia article on this award.

The Prize will be awarded annually at a major computer vision conference. In even numbered years it will be awarded at the European Conference on Computer Vision (ECCV), and in odd numbered years it will be awarded at the International Conference on Computer Vision (ICCV).

Outline of the Prize

The Prize will be awarded annually at a major computer vision conference. In even numbered years it will be awarded at the European Conference on Computer Vision (ECCV), and in odd numbered years it will be awarded at the International Conference on Computer Vision (ICCV).

The Everingham Prize will be awarded to a Researcher or Research Team (the Recipient) who has made a significant contribution to the computer vision community. For example, this contribution may be:

• Running a competition or challenge which has seen significant uptake by the community and has allowed a standardized comparison between methods and tracking the progress of the community as a whole;
• Creating open source software or shared library component that has seen significant use within the community, enabling many researchers to build more capable vision systems;
• Creating a public dataset that has seen significant use within the community and has opened up new areas of research and/or allowed standardized comparison between methods;
• Creating an online service, text book or other resource which has been of significant help to computer vision researchers in pursuing their research;
• Performing research that analyses many methods within the community to provide significant new insights into the characteristics of existing methods, particularly if such insights suggest new avenues of research.

The above examples are not exhaustive – the Prize is open to any contribution judged to be a significant benefit to the computer vision community according to the Selection Criteria. Contributions may have occurred at any time prior to nomination – they are not limited to contributions since the previous Prize was awarded.
The Prize consists of a Gift to the Recipient of USD 3,000 which will be presented during the conference, along with a plaque.
Nomination process and timescale

Two methods of nomination are permitted:

• An individual or team may nominate themselves,
• Proposers may submit a nomination for another individual or team.

Previous winners are not excluded from nomination, but the nomination must be for a new contribution.
Nominations should be made by email to at least two months before the relevant ECCV or ICCV. The nomination should include name(s) and contact detail(s) of the individual or team (the Candidate), a brief description of the contribution and should address the following four selection criteria (quantitatively where possible):

• The impact of the contribution – in terms of the degree of benefit to the community and the number of community members benefitting,
• The time and effort that went into the contribution,
• The degree to which the Candidate sought to understand and address the needs of the community – particularly those members whose needs have previously been overlooked,
• The lasting nature of the contribution – the length of time over which the community has actively benefitted, or is expected to benefit looking ahead.

Eligibility of the Candidate

For the purpose of this Prize we define a Researcher or Research Team to be anyone actively involved in research at a University, other research institution in the public or private sector, or in industry.

The Prize is open to Researchers at any stage in their career, but particular emphasis will be given to contributions by Researchers in the earlier stages of their career.


2021 The Detectron object detection and segmentation software “For developing and maintaining the Detectron object detection and segmentation software.” R. Girshick, Y. Wu, I. Radosavovic, A. Kirillov, G. Gkioxari,
F. Massa, W.-Y. Lo, P. Dollár, K. He and Team
2021 The KITTI Vision Benchmark “For developing and maintaining the KITTI Vision Benchmark.” A. Geiger, P. Lenz, C. Stiller, R. Urtasun and Team
2020 COLMAP SFM and MVS Software Library “For developing and maintaining the COLMAP SFM and MVS software library.” J. Schönberger
2020 Antonio Torralba “For the developing and maintaining multiple datasets in the field of computer vision.” A. Torralba
2019 Labeled Faces in the Wild (LFW) “For generating and maintaining the LFW dataset and benchmark, starting from 2007. LFW has helped drive the field towards more uncontrolled and real-world face recognition.” E. Learned-Miller, G. B. Huang, T. Berg and Team
2019 Gerard Medioni “For extensive and sustained contributions to CVPR & ICCV conference organization over several decades, and multiple other services to the community. He also introduced the unifying passport registration system for conferences and workshops, and was a co-founder of the Computer Vision Foundation.” G. Medioni
2018 TRECVid Video Retrieval Evaluation 2003-18 (datasets and workshops) “For a series of datasets and workshops since 2003 that have driven progress in large scale Video Retrieval.” A. Smeaton, W. Kraaij, P. Over, G. Awad
2018 VisualSFM software library “For providing a well documented software library for Structure from Motion that has been used effortlessly by so many.” C. Wu
2017 Caffe “For providing an open-source deep learning framework that enabled the community to use, train and share deep convolutional neural networks. Caffe has had a huge impact, both academic and commercial” Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell
2017 Int. Computer Vision Summer School (ICVSS) 2008-17 “For a series of annual computer vision Summer schools that have brought such benefit to the students attending them, both educationally and socially” S. Battiato, R. Cipolla, and G. Farinella
2016 Ramin Zabih “For extensive, generous, service to the community: As long-term head of the IEEE PAMI Technical Committee he introduced many reforms, including to the awards process and the relationship to the IEEE. And he has been the driving force in creating and running the Computer Vision Foundation (CVF)” R. Zabih
2016 ImageNet “For a series of datasets and challenges since 2010 that have had such impact on the computer vision field. ImageNet built on the Caltech101/256 datasets, increasing the number of images by orders of magnitude and enabling the development of new algorithms” A. Berg, J. Deng, F.-F. Li, O. Russakovsky and team
2015 VLFeat Software “For providing a well documented library of open source software for image understanding and matching that has been effective in the development of new algorithms and applications” A. Vedaldi
2015 Middlebury Dataset “For a series of datasets and on-line evaluations starting with Stereo datasets in the 2001 and extending to Optic flow, MRF and others, that have inspired many other datasets” D. Scharstein, R. Szeliski
2014 Terry and Ginger Boult “For extensive, generous, long-term service to the community in the management of computer vision conferences and workshops.” G. Boult, T. Boult
2013 FERET and FRVT face datasets and challenges “For a series of datasets and challenges starting with FERET in the 1990s and extending to FRVT 2000-2016” P. Jonathon Phillips
2013 OpenCV “For providing a huge wealth of open source software that has been of such benefit both inside and outside the computer vision field” G. Bradski and team