3D object proposals for accurate object class detection
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w8711335 →Countries where authors are citing 3D object proposals for accurate object class detection
This map shows the geographic impact of 3D object proposals for accurate object class detection. 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 3D object proposals for accurate object class detection with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites 3D object proposals for accurate object class detection more than expected).
Fields of papers citing 3D object proposals for accurate object class detection
This network shows the impact of 3D object proposals for accurate object class detection. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the 3D object proposals for accurate object class detection.
About 3D object proposals for accurate object class detection
This paper, published in 2015, received 426 indexed citations . Written by Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Huimin Ma, Sanja Fidler and Raquel Urtasun covering the research area of Aerospace Engineering and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (387 citations), Aerospace Engineering (203 citations) and Automotive Engineering (84 citations). Published in Neural Information Processing Systems.
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/w8711335.