Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective
- Journal
- ACM Computing Surveys
In The Last Decade
doi.org/10.1145/3524497 →Countries where authors are citing Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective
This map shows the geographic impact of Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective. 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 Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective more than expected).
Fields of papers citing Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective
This network shows the impact of Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective.
About Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective
This paper, published in 2022, received 139 indexed citations . Written by Wu Liu, Qian Bao, Yu Sun and Tao Mei covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (100 citations), Biomedical Engineering (48 citations) and Artificial Intelligence (29 citations). Published in ACM Computing Surveys.
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/10.1145/3524497.