Multimodal Transformer With Multi-View Visual Representation for Image Captioning
Impact in
Classified as
- Authors
- Jun YuYu ZhouQingming Huang
In The Last Decade
doi.org/10.1109/tcsvt.2019.2947482 →Countries where authors are citing Multimodal Transformer With Multi-View Visual Representation for Image Captioning
This map shows the geographic impact of Multimodal Transformer With Multi-View Visual Representation for Image Captioning. 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 Multimodal Transformer With Multi-View Visual Representation for Image Captioning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Multimodal Transformer With Multi-View Visual Representation for Image Captioning more than expected).
Fields of papers citing Multimodal Transformer With Multi-View Visual Representation for Image Captioning
This network shows the impact of Multimodal Transformer With Multi-View Visual Representation for Image Captioning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Multimodal Transformer With Multi-View Visual Representation for Image Captioning.
About Multimodal Transformer With Multi-View Visual Representation for Image Captioning
This paper, published in 2019, received 371 indexed citations . Written by Jun Yu, Yu Zhou and Qingming Huang covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (294 citations), Artificial Intelligence (145 citations), Signal Processing (17 citations), Media Technology (17 citations) and Computer Networks and Communications (17 citations). Published in IEEE Transactions on Circuits and Systems for Video 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/10.1109/tcsvt.2019.2947482.