Estimating the Semantic Density of Visual Media

Image descriptions provide precious information for a myriad of visual media management tasks ranging from image classification to image search. The value of such curated collections comes from their diverse content and their accompanying extensive annotations. Such annotations are typically supplied by communities, where users (often volunteers) curate labels and/or descriptions of images. Supporting users in their quest to increase (overall) description completeness where possible is, therefore, of utmost importance.

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Publication Details

Published
2024-10-26
DOI
https://doi.org/10.1145/3664647.3681594
Citations
1
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.25
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Estimating the Semantic Density of Visual Media

Cristina Sarasua, Luca Rossetto, Abraham Bernstein
1 citations
Multimodal Machine Learning Applications
0.25
article

Estimating the Semantic Density of Visual Media

Cristina Sarasua, Luca Rossetto, Abraham Bernstein
article en
1 citations

Abstract

Image descriptions provide precious information for a myriad of visual media management tasks ranging from image classification to image search. The value of such curated collections comes from their diverse content and their accompanying extensive annotations. Such annotations are typically supplied by communities, where users (often volunteers) curate labels and/or descriptions of images. Supporting users in their quest to increase (overall) description completeness where possible is, therefore, of utmost importance.

University of Zurich (CH)
Reduced inequalities
Openalex Percentile: Top 46%
Multimodal Machine Learning Applications
0.25
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