Sina Honari

735 total citations
15 papers, 165 citations indexed

About

Sina Honari is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Sina Honari has authored 15 papers receiving a total of 165 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 2 papers in Automotive Engineering. Recurrent topics in Sina Honari's work include Human Pose and Action Recognition (4 papers), Video Surveillance and Tracking Methods (4 papers) and Generative Adversarial Networks and Image Synthesis (3 papers). Sina Honari is often cited by papers focused on Human Pose and Action Recognition (4 papers), Video Surveillance and Tracking Methods (4 papers) and Generative Adversarial Networks and Image Synthesis (3 papers). Sina Honari collaborates with scholars based in Switzerland, Canada and United States. Sina Honari's co-authors include Pascal Fua, Shangchen Han, Edoardo Remelli, Robert Wang, Mathieu Salzmann, Christopher Pal, Christopher Beckham, R Devon Hjelm, Joseph Cohen and Pascal Vincent and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and International Journal of Computer Vision.

In The Last Decade

Sina Honari

13 papers receiving 159 citations

Peers

Sina Honari
Comparison fields: 5 of 42
  • Computer Vision and Pattern Recognition 138
  • Artificial Intelligence 45
  • Biomedical Engineering 25
  • Endocrinology, Diabetes and Metabolism 24
  • Human-Computer Interaction 22
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Umer Rafi Germany View profile →
Citations per field, relative to Sina Honari
Sina Honari · 1×
Citations per year, relative to Sina Honari
Sina Honari · 1×

Countries citing papers authored by Sina Honari

Since Specialization
Citations

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

Fields of papers citing papers by Sina Honari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sina Honari

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

All Works

15 of 15 papers shown
# Work Indexed citations
1 0
2 5
3 16
4 4
5 9
6 1
7 9
8 82
9
Adversarial Mixup Resynthesizers
9
10 5
11 8
12
How to Cure Cancer (in images) with Unpaired Image Translation
7
13 6
14 4
15 0

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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