Patrick Poirson

1.3k total citations
3 papers, 125 citations indexed

About

Patrick Poirson is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Patrick Poirson has authored 3 papers receiving a total of 125 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Vision and Pattern Recognition, 1 paper in Computer Networks and Communications and 1 paper in Information Systems. Recurrent topics in Patrick Poirson's work include Recommender Systems and Techniques (1 paper), Visual Attention and Saliency Detection (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). Patrick Poirson is often cited by papers focused on Recommender Systems and Techniques (1 paper), Visual Attention and Saliency Detection (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). Patrick Poirson collaborates with scholars based in United States. Patrick Poirson's co-authors include Jana Košecká, Alexander C. Berg, Eunbyung Park, Hanyu Wang, Pengxiang Wu, Chen Wang, Abhinav Shrivastava and Chen Wang and has published in prestigious journals such as arXiv (Cornell University).

In The Last Decade

Patrick Poirson

3 papers receiving 123 citations

Peers

Patrick Poirson

CVPR

AI

AE

CSE

SP

Shenyuan Gao Hong Kong

Y. Ono Japan

Vincent Cartillier United States

Yujie Zhong China

Ilchae Jung South Korea

Johan Edstedt Sweden

Mårten Wadenbäck Sweden

Erika Lu United States

Yumin Suh United States

Sébastien Ehrhardt United Kingdom

Shenyuan Gao Hong Kong

Patrick Poirson

99 ×1.0

CVPR

21 ×0.5

AI

32 ×0.7

AE

9 ×0.7

CSE

3 ×0.4

SP

Citations per year, relative to Patrick Poirson Patrick Poirson (= 1×) peers Shenyuan Gao

Countries citing papers authored by Patrick Poirson

Since Specialization

Citations

This map shows the geographic impact of Patrick Poirson'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 Poirson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick Poirson more than expected).

Fields of papers citing papers by Patrick Poirson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Patrick Poirson. 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 Poirson. The network helps show where Patrick Poirson may publish in the future.

Co-authorship network of co-authors of Patrick Poirson

This figure shows the co-authorship network connecting the top 25 collaborators of Patrick Poirson. A scholar is included among the top collaborators of Patrick Poirson 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 Poirson. Patrick Poirson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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3 of 3 papers shown

1.

Wang, Hanyu, et al.. (2023). NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-Wise Modeling. 14378–14387. 18 indexed citations

2.

Poirson, Patrick, et al.. (2022). Revisiting Neighborhood-based Link Prediction for Collaborative Filtering. arXiv (Cornell University). 1009–1018. 7 indexed citations

3.

Poirson, Patrick, et al.. (2017). A dataset for developing and benchmarking active vision. 1378–1385. 100 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.

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