RGB-Infrared Cross-Modality Person Re-identification
Impact in
Classified as
- Journal
- Queen Mary Research Online (Queen Mary University of London)
In The Last Decade
doi.org/10.1109/iccv.2017.575 →Countries where authors are citing RGB-Infrared Cross-Modality Person Re-identification
This map shows the geographic impact of RGB-Infrared Cross-Modality Person Re-identification. 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 RGB-Infrared Cross-Modality Person Re-identification with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites RGB-Infrared Cross-Modality Person Re-identification more than expected).
Fields of papers citing RGB-Infrared Cross-Modality Person Re-identification
This network shows the impact of RGB-Infrared Cross-Modality Person Re-identification. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the RGB-Infrared Cross-Modality Person Re-identification.
About RGB-Infrared Cross-Modality Person Re-identification
This paper, published in 2017, received 644 indexed citations . Written by Ancong Wu, Wei‐Shi Zheng, Hong-Xing Yu, Shaogang Gong and J. Lai covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (606 citations), Biomedical Engineering (133 citations), Aerospace Engineering (73 citations), Global and Planetary Change (55 citations) and Safety, Risk, Reliability and Quality (38 citations). Published in Queen Mary Research Online (Queen Mary University of London).
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.
This paper is also available at doi.org/10.1109/iccv.2017.575.