Generative Image Inpainting with Contextual Attention

1.5k indexed citations

Abstract

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About

This paper, published in 2018, received 1.5k indexed citations. Written by Jiahui Yu, Zhe Lin, Shuicheng Yan, Xiaohui Shen, Xin Lu and Thomas S. Huang covering the research area of Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (1.3k citations), Computer Graphics and Computer-Aided Design (172 citations) and Artificial Intelligence (164 citations). Published in .

Countries where authors are citing Generative Image Inpainting with Contextual Attention

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This map shows the geographic impact of Generative Image Inpainting with Contextual Attention. 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 Generative Image Inpainting with Contextual Attention with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Generative Image Inpainting with Contextual Attention more than expected).

Fields of papers citing Generative Image Inpainting with Contextual Attention

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Generative Image Inpainting with Contextual Attention. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Generative Image Inpainting with Contextual Attention.

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/cvpr.2018.00577.

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