Learning Deep Features for Scene Recognition using Places Database
- Journal
- DSpace@MIT (Massachusetts Institute of Technology)
In The Last Decade
doi.org/w50973832 →Countries where authors are citing Learning Deep Features for Scene Recognition using Places Database
This map shows the geographic impact of Learning Deep Features for Scene Recognition using Places Database. 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 Learning Deep Features for Scene Recognition using Places Database with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Learning Deep Features for Scene Recognition using Places Database more than expected).
Fields of papers citing Learning Deep Features for Scene Recognition using Places Database
This network shows the impact of Learning Deep Features for Scene Recognition using Places Database. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Learning Deep Features for Scene Recognition using Places Database.
About Learning Deep Features for Scene Recognition using Places Database
This paper, published in 2014, received 1.6k indexed citations . Written by Bolei Zhou, Àgata Lapedriza, Jianxiong Xiao, Antonio Torralba and Aude Oliva 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 (1.3k citations), Artificial Intelligence (451 citations) and Aerospace Engineering (193 citations). Published in DSpace@MIT (Massachusetts Institute of Technology).
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/w50973832.