CUTIE: A human-in-the-loop interface for the generation of personalised and contextualised image captions

Image captioning is an AI-complete task that bridges computer vision and natural language processing. Its goal is to generate textual descriptions for a given image. However, general-purpose image captioning often does not capture contextual information, such as information about the people present or the location the image was shot. To address this challenge, we propose a web-based tool that leverages automated image captioning, large foundation models, and additional deep learning modules such as object recognition and metadata analysis to accelerate the process of generating contextualised and personalised image captions. The tool allows users to create personalised and contextualised image captions efficiently. User interactions and feedback given to the various components are stored and later used for domain adaptation of the respective components. Our ultimate goal is to improve the efficiency and accuracy of creating personalised and contextualised image captions.

Publication Details

Journal
Gesellschaft für Informatik (GI)
Published
2025-01-01
DOI
https://doi.org/10.18420/muc2025-mci-demo-261
Primary Topic
Diverse Scientific and Economic Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

CUTIE: A human-in-the-loop interface for the generation of personalised and contextualised image captions

Gesellschaft für Informatik (GI)
Diverse Scientific and Economic Studies
article

CUTIE: A human-in-the-loop interface for the generation of personalised and contextualised image captions

article en

Abstract

Image captioning is an AI-complete task that bridges computer vision and natural language processing. Its goal is to generate textual descriptions for a given image. However, general-purpose image captioning often does not capture contextual information, such as information about the people present or the location the image was shot. To address this challenge, we propose a web-based tool that leverages automated image captioning, large foundation models, and additional deep learning modules such as object recognition and metadata analysis to accelerate the process of generating contextualised and personalised image captions. The tool allows users to create personalised and contextualised image captions efficiently. User interactions and feedback given to the various components are stored and later used for domain adaptation of the respective components. Our ultimate goal is to improve the efficiency and accuracy of creating personalised and contextualised image captions.

Gesellschaft für Informatik (GI)
Quality Education
Openalex Percentile: Top 36%
Diverse Scientific and Economic Studies
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