Easy Travelogue: A Travelogue Editor with Automatic Image Recommendation and Insertion
Authors: 
Fan Yu, Huanyu Xing, Jia Bei, Tongwei RenAuthors Info & Claims
Fan Yu, Huanyu Xing, Jia Bei, Tongwei RenAuthors Info & ClaimsArticle No.: 105, Pages 1 - 3
Abstract
Travelogues are a common media form that incorporates both text and images. Typically, they are composed after the completion of a travel period. Creating a travelogue demands substantial time and effort, particularly in the curation of suitable images from the extensive collection of photos taken during the journey to complement the text. Consequently, we have developed and implemented Easy Travelogue, a travelogue editor that utilizes visual and language models. It offers real-time image suggestions while writing the text and can automatically insert fitting images into the finished content. The editor is versatile and can be readily utilized for personal travelogues, travel blogs, and various social media platforms, facilitating users in effortlessly sharing and showcasing their travel experiences.
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References
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Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning. PMLR, 4904–4916.
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- Easy Travelogue: A Travelogue Editor with Automatic Image Recommendation and Insertion
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Information & Contributors
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Published In
December 2023
745 pages
ISBN:9798400702051
DOI:10.1145/3595916
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Publication History
Published: 01 January 2024
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- Demonstration
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China
- the Collaborative Innovation Center of Novel Software Technology and Industrialization
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MMAsia '23
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Overall Acceptance Rate 59 of 204 submissions, 29%
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