Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis
Quick Take
The article introduces Dimensional Distribution Emotion State (DDES), a novel representation for predicting emotional responses to artworks, enhancing deep learning model training. This approach aims to streamline the emotion extraction process for museum exhibitions, reducing curator bias and labor intensity while maintaining competitive performance compared to traditional methods.
Key Points
- DDES enhances emotion representation in visual art analysis through a continuous bi-dimensional space.
- The proposed method reduces the need for labor-intensive manual annotation by curators.
- DDES shows advantages over existing categorical and dimensional emotion models.
- The approach aims to democratize access to art by engaging a wider audience.
- Multi-dataset training pipeline is introduced to improve model performance.
Article Content
From source RSS / original summaryarXiv:2605. 26262v1 Announce Type: new 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.
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