Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis
Researchers propose Dimensional Distribution Emotion State (DDES), a new emotion representation using valence and arousal to predict emotional responses to artworks, aiding museum curators in designing emotion-based exhibitions.
Article intelligence
Key points
- Emotion-based exhibitions in museums aim to increase engagement and democratize art access.
- Manual annotation of artworks is labor-intensive and biased; DDES automates emotion prediction.
- DDES leverages a continuous bi-dimensional emotion space (valence and arousal) for deep learning.
- The approach shows advantages over existing representations with similar baseline performance.
Why it matters
This matters because emotion-based exhibitions in museums aim to increase engagement and democratize art access.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26262] Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis
[Submitted on 25 May 2026]
Title:Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis
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Abstract:Museums are important sites for the dissemination of culture and art. They are institutions rooted in history and tradition; their exhibitions are often designed to highlight these aspects. Recently, a new approach is being explored in the field: emotion-based exhibitions. These exhibitions are designed specifically to elicit emotions in the visitors, in order to maximize engagement, and as a way to democratize access to art and attract a wider, more diverse audience. To do so, the emotional content of the artworks must first be extracted, however, manually annotating the artworks by experts is a prohibitively labor-intensive process, and risks introducing the personal bias of curators. To assist the museum curators in their design of these exhibitions, we wish to develop a tool that can predict the emotional response evoked by a work of art. In this article, we leverage a continuous bi-dimensional emotion space to enhance emotion representations and the training process of deep learning models. Drawing inspiration from existing categorical and dimensional emotion representations, we introduce a new representation, Dimensional Distribution Emotion State (DDES), along with a pipeline for multi-dataset training. We show that DDES provides multiple advantages compared to widely used representations while exhibiting similar baseline performance.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.26262 [cs.CV]
(or arXiv:2605.26262v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.26262
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Émile Bergeron [view email] [v1] Mon, 25 May 2026 18:44:26 UTC (7,757 KB)
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