用符号同构思想构建更大更准的情感图像数据集，提升模型泛化能力。
Enhancing Visual Sentiment Analysis via Semiotic Isotopy-Guided Dataset Construction
- 引入符号同构概念优化数据集构造，增强情感元素关联性
- 新数据集训练模型在主流基准上性能全面超越原数据集
- 适合做视觉情感分析、跨数据集泛化的研究者参考
视觉情感分析（VSA）因情感显著图像种类繁多，且难以获取足够数据以全面覆盖其变异性而面临挑战。主要难点包括构建大规模VSA数据集，以及发展能有效识别图像中情感相关要素组合的方法。这些问题导致现有VSA模型在不同数据集间训练与测试时泛化能力有限。本文从已有数据集出发，通过将符号同构（semiotic isotopy）概念融入数据构建流程，生成一个规模更大、图像多样性更高的新数据集。该数据集不仅包含更丰富的图像类型，还使训练出的新模型能更准确聚焦于情感相关的图像元素组合。实证评估表明，基于本方法生成的数据集训练的模型，在多个主流VSA基准上均表现更优，展现出更强的跨数据集泛化能力。
原文摘要 · Abstract (English)
Visual Sentiment Analysis (VSA) is a challenging task due to the vast diversity of emotionally salient images and the inherent difficulty of acquiring sufficient data to capture this variability comprehensively. Key obstacles include building large-scale VSA datasets and developing effective methodologies that enable algorithms to identify emotionally significant elements within an image. These challenges are reflected in the limited generalization performance of VSA algorithms and models when trained and tested across different datasets. Starting from a pool of existing data collections, our approach enables the creation of a new larger dataset that not only contains a wider variety of images than the original ones, but also permits training new models with improved capability to focus on emotionally relevant combinations of image elements. This is achieved through the integration of the semiotic isotopy concept within the dataset creation process, providing deeper insights into the emotional content of images. Empirical evaluations show that models trained on a dataset generated with our method consistently outperform those trained on the original data collections, achieving superior generalization across major VSA benchmarks
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