Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The Pascal Visual Object Classes (VOC) Challenge
200912.0k citationsMark Everingham, Luc Van Gool et al.International Journal of Computer Visionprofile →
The Pascal Visual Object Classes Challenge: A Retrospective
20144.4k citationsMark Everingham, S. M. Ali Eslami et al.International Journal of Computer Visionprofile →
Countries citing papers authored by Mark Everingham
Since
Specialization
Citations
This map shows the geographic impact of Mark Everingham's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark Everingham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Everingham more than expected).
This network shows the impact of papers produced by Mark Everingham. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark Everingham. The network helps show where Mark Everingham may publish in the future.
Co-authorship network of co-authors of Mark Everingham
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Everingham.
A scholar is included among the top collaborators of Mark Everingham based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Mark Everingham. Mark Everingham is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Everingham, Mark, S. M. Ali Eslami, Luc Van Gool, et al.. (2013). Assessing the significance of performance differences on the PASCAL VOC challenges via bootstrapping. Lirias (KU Leuven).1 indexed citations
Sun, Min, Silvio Savarese, Mark Everingham, et al.. (2011). Technical Report: Articulated Part-based Model for Joint Object Detection and Pose Estimation.1 indexed citations
6.
Johnson, P. Sam & Mark Everingham. (2010). Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. 12.1–12.11.485 indexed citations breakdown →
7.
Everingham, Mark, Luc Van Gool, Christopher K. I. Williams, John Winn, & Andrew Zisserman. (2009). The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision. 88(2). 303–338.12010 indexed citations breakdown →
Everingham, Mark, Josef Šivic, & Andrew Zisserman. (2006). Hello! My name is... Buffy'' -- Automatic Naming of Characters in TV Video. 92.1–92.10.370 indexed citations breakdown →
Everingham, Mark, et al.. (1998). A neural-network virtual-reality mobility aid for the severely visually impaired. Bristol Research (University of Bristol). 14–14.8 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.