用情绪维度建模艺术作品，自动预测观众情感反应。
Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis
- 引入双维度情绪状态（DDES）表示法，融合愉悦度与唤醒度。
- 在多数据集上训练，显著提升模型对艺术情感的捕捉能力。
- 适合博物馆策展与艺术数字化研究者使用。
博物馆是文化传播的重要场所，近年来兴起以情绪为导向的展览设计，旨在通过激发观众情感来增强参与感并扩大受众覆盖面。然而，人工标注艺术品情感成本高且易受策展人主观偏见影响。为此，本文提出一种基于连续双维度情绪空间（愉悦度与唤醒度）的新型表示方法——维度分布情绪状态（DDES），用于增强深度学习模型的情感表征能力与训练效果。该方法融合了分类与连续情绪表示的优势，构建了多数据集联合训练流程。实验表明，DDES在保持与现有方法相当基线性能的同时，显著提升了情绪表征的表达力与泛化能力。
原文摘要 · Abstract (English)
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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