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.
Aggregating Local Image Descriptors into Compact Codes
20111.0k citationsPatrick Pérez et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images
This map shows the geographic impact of Patrick Pérez'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 Patrick Pérez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick Pérez more than expected).
This network shows the impact of papers produced by Patrick Pérez. 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 Patrick Pérez. The network helps show where Patrick Pérez may publish in the future.
Co-authorship network of co-authors of Patrick Pérez
This figure shows the co-authorship network connecting the top 25 collaborators of Patrick Pérez.
A scholar is included among the top collaborators of Patrick Pérez 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 Patrick Pérez. Patrick Pérez is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Thome, Nicolas, et al.. (2019). Addressing Failure Prediction by Learning Model Confidence. arXiv (Cornell University).118 indexed citations
8.
Mikšík, Ondřej, Vibhav Vineet, Matthias Nießner, et al.. (2015). The Semantic Paintbrush. Human Factors in Computing Systems.2 indexed citations
9.
McGuinness, Kevin, Robin Aly, Ken Chatfield, et al.. (2014). The AXES research video search system. Arrow@dit (Dublin Institute of Technology).5 indexed citations
10.
Mikšík, Ondřej, Vibhav Vineet, Patrick Pérez, & Philip H. S. Torr. (2014). Distributed Non-convex ADMM-based inference in large-scale random fields.. Oxford University Research Archive (ORA) (University of Oxford).14 indexed citations
11.
Jurie, Frédéric, et al.. (2014). EPML: Expanded Parts based Metric Learning for Occlusion Robust Face Verification. HAL (Le Centre pour la Communication Scientifique Directe).2 indexed citations
12.
Criminisi, Antonio, Toby Sharp, Carsten Rother, & Patrick Pérez. (2011). Geodesic Image and Video Editing.30 indexed citations
Collet, Christophe, et al.. (1999). Unsupervised multispectral segmentation of SPOT images applied to nautical cartography.. 622–626.1 indexed citations
18.
Pérez, Patrick. (1998). Markov Random Fields and Images. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands.55 indexed citations
Pérez, Patrick & Fabrice Heitz. (1994). Restriction of a Markov random field on a graph and multiresolution image analysis. OpenGrey (Institut de l'Information Scientifique et Technique). 5(8). 15–7.7 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.