Joint Detection and Identification Feature Learning for Person Search
Impact in
Classified as
- Authors
- Tong XiaoShuang LiBochao WangLiang LinXiaogang Wang
In The Last Decade
doi.org/10.1109/cvpr.2017.360 →Countries where authors are citing Joint Detection and Identification Feature Learning for Person Search
This map shows the geographic impact of Joint Detection and Identification Feature Learning for Person Search. 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 Joint Detection and Identification Feature Learning for Person Search with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joint Detection and Identification Feature Learning for Person Search more than expected).
Fields of papers citing Joint Detection and Identification Feature Learning for Person Search
This network shows the impact of Joint Detection and Identification Feature Learning for Person Search. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Joint Detection and Identification Feature Learning for Person Search.
About Joint Detection and Identification Feature Learning for Person Search
This paper, published in 2017, received 690 indexed citations . Written by Tong Xiao, Shuang Li, Bochao Wang, Liang Lin and Xiaogang Wang covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (661 citations), Biomedical Engineering (184 citations), Artificial Intelligence (71 citations), Safety, Risk, Reliability and Quality (55 citations) and Aerospace Engineering (42 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.
This paper is also available at doi.org/10.1109/cvpr.2017.360.