Computer Vision and Pattern Recognition

1.1M papers and 19.7M indexed citations i.

About

1.1M papers covering Computer Vision and Pattern Recognition have received a total of 19.7M indexed citations since 1950. Papers on subfields are most often about the specific topic of Advanced Vision and Imaging, Image and Signal Denoising Methods and Advanced Image and Video Retrieval Techniques and also cover the fields of Artificial Intelligence, Signal Processing and Media Technology. Papers citing papers on subfields are usually about Artificial Intelligence, Media Technology and Aerospace Engineering. Some of the most active scholars covering Computer Vision and Pattern Recognition are Leo Breiman, Geoffrey E. Hinton, Kaiming He, Shaoqing Ren, Yoshua Bengio, David L. Donoho, Andrew Zisserman, David Lowe, Xiangyu Zhang and Vladimir Vapnik.

In The Last Decade

Fields of papers citing papers about Computer Vision and Pattern Recognition

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers covering Computer Vision and Pattern Recognition. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers covering Computer Vision and Pattern Recognition.

Countries where authors publish papers about Computer Vision and Pattern Recognition

Since Specialization
Citations

This map shows the geographic impact of research in Computer Vision and Pattern Recognition. 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 papers about Computer Vision and Pattern Recognition with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Computer Vision and Pattern Recognition more than expected).

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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2025