Shared Latent Space of Font Shapes and Their Noisy Impressions

Jihun Kang, Daichi Haraguchi, Seiya Matsuda, Akisato Kimura, Seiichi Uchida

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Styles of typefaces or fonts are often associated with specific impressions, such as heavy, contemporary, or elegant. This indicates that there are certain correlations between font shapes and their impressions. To understand the correlations, this paper constructs a shared latent space where a font and its impressions are embedded nearby. The difficulty is that the impression words attached to a font are often very noisy. This is because impression words are very subjective and diverse. More importantly, some impression words have no direct relevance to the font shapes and will disturb the construction of the shared latent space. We, therefore, use DeepSets for enhancing shape-relevant words and suppressing shape irrelevant words automatically while training the shared latent space. Quantitative and qualitative experimental results with a large-scale font-impression dataset demonstrate that the shared latent space by the proposed method describes the correlation appropriately, especially for the shape-relevant impression words.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 28th International Conference, MMM 2022, Proceedings
EditorsBjörn Þór Jónsson, Cathal Gurrin, Minh-Triet Tran, Duc-Tien Dang-Nguyen, Anita Min-Chun Hu, Binh Huynh Thi Thanh, Benoit Huet
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030983543
Publication statusPublished - 2022
Event28th International Conference on MultiMedia Modeling, MMM 2022 - Phu Quoc, Viet Nam
Duration: Jun 6 2022Jun 10 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13142 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on MultiMedia Modeling, MMM 2022
Country/TerritoryViet Nam
CityPhu Quoc

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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