Multiple Instance Boosting for Object Detection
- Authors
- Cha ZhangJohn PlattPaul Viola
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w5551455 →Countries where authors are citing Multiple Instance Boosting for Object Detection
This map shows the geographic impact of Multiple Instance Boosting for Object 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 Multiple Instance Boosting for Object Detection with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Multiple Instance Boosting for Object Detection more than expected).
Fields of papers citing Multiple Instance Boosting for Object Detection
This network shows the impact of Multiple Instance Boosting for Object Detection. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Multiple Instance Boosting for Object Detection.
About Multiple Instance Boosting for Object Detection
This paper, published in 2005, received 446 indexed citations . Written by Cha Zhang, John Platt and Paul Viola covering the research area of Media Technology and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (402 citations), Artificial Intelligence (144 citations) and Media Technology (43 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/w5551455.