Natalia Neverova

96 total papers · 3.2k total citations
35 papers, 602 citations indexed

About

Natalia Neverova is a scholar working on Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism and Artificial Intelligence. According to data from OpenAlex, Natalia Neverova has authored 35 papers receiving a total of 602 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 6 papers in Endocrinology, Diabetes and Metabolism and 4 papers in Artificial Intelligence. Recurrent topics in Natalia Neverova's work include Human Pose and Action Recognition (9 papers), Advanced Vision and Imaging (7 papers) and Thyroid Disorders and Treatments (5 papers). Natalia Neverova is often cited by papers focused on Human Pose and Action Recognition (9 papers), Advanced Vision and Imaging (7 papers) and Thyroid Disorders and Treatments (5 papers). Natalia Neverova collaborates with scholars based in United States, France and Canada. Natalia Neverova's co-authors include Andrea Vedaldi, Hanbyul Joo, Jack L. Feldman, Yossi Adi, Moustapha Cissé, Joseph Keshet, Christian Wolf, Gengshan Yang, Minh Vo and Deva Ramanan and has published in prestigious journals such as Journal of Neuroscience, IEEE Transactions on Pattern Analysis and Machine Intelligence and The Journal of Clinical Endocrinology & Metabolism.

In The Last Decade

Natalia Neverova

34 papers receiving 591 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Natalia Neverova 331 123 105 95 64 35 602
Alark Joshi 254 0.8× 31 0.3× 78 0.7× 6 0.1× 23 0.4× 48 599
Chenxi Liu 252 0.8× 71 0.6× 177 1.7× 6 0.1× 12 0.2× 51 508
Lei Li 283 0.9× 155 1.3× 54 0.5× 3 0.0× 30 0.5× 29 613
Patrick Marais 214 0.6× 195 1.6× 33 0.3× 4 0.0× 63 1.0× 47 660
Wen‐Hung Liao 349 1.1× 40 0.3× 101 1.0× 2 0.0× 33 0.5× 57 646
Zi Wang 154 0.5× 19 0.2× 114 1.1× 24 0.3× 28 0.4× 41 626
Shu‐Yen Wan 227 0.7× 41 0.3× 42 0.4× 43 0.5× 21 0.3× 29 538
Miguel Ángel García 397 1.2× 51 0.4× 61 0.6× 3 0.0× 24 0.4× 66 595
Vinh‐Thong Ta 271 0.8× 23 0.2× 72 0.7× 9 0.1× 11 0.2× 30 619
Boyi Jiang 264 0.8× 174 1.4× 11 0.1× 6 0.1× 27 0.4× 24 548

Countries citing papers authored by Natalia Neverova

Since Specialization
Citations

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

Fields of papers citing papers by Natalia Neverova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Natalia Neverova

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

All Works

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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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