用自监督模型提取画作特征，分类效果更优。
Harnessing Self-Supervised Features for Art Classification
- 采用自监督模型作为特征提取器，提升画作分类能力。
- 相比监督模型，自监督方法在多个指标上表现更稳定。
- 适合用于虚拟现实博物馆等实际场景中的艺术检索。
艺术作品分类面临挑战，因其细节丰富且风格抽象。本文系统评估了监督与自监督骨干网络作为特征提取器在绘画分类与检索中的表现，使用DINO家族和CLIP模型进行多策略实验。结果表明，自监督骨干网络显著提升分类性能，并为虚拟现实博物馆导航等真实应用场景提供了可行性参考。
原文摘要 · Abstract (English)
Classifying artworks presents a significant challenge due to the complex interplay of fine-grained details and abstract features that condition the style or genre of an artwork. This paper presents a systematic investigation of the effectiveness of supervised and self-supervised backbones as feature extractors for both artwork classification and retrieval, with a particular focus on paintings. We conduct an extensive experimental evaluation using the DINO family and CLIP models, assessing multiple classification strategies and feature representations. Our results demonstrate that employing a self-supervised backbone leads to consistent improvements in artwork classification performance. Moreover, our work provides insights into the applicability of classification and retrieval modules in real-world applications, such as virtual reality (VR) applications that support museum navigation.
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