新颖的艺术场景中心数据集：面向芬芳空间有效迁移学习
Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces
AI总结：
针对文化遗产中嗅觉场景，提出用弱标注数据微调Places365模型，显著提升芬芳空间与艺术场景分类，并发布ArtPlaces数据集。
摘要：
嗅觉在文化遗产研究中常被忽视，但在塑造人类体验和身份方面具有深远意义。审视历史中嗅觉场景的描绘，可以为气味在历史中的作用提供宝贵见解。我们证明，使用弱标注训练数据的迁移学习方法能显著提升芬芳空间分类，以及更广泛的艺术场景描绘分类。我们通过查询两个文化遗产数据源，并使用搜索词作为监督信号，对Places365预训练模型进行微调。模型在两个手动校正的测试集上进行了评估。这项工作为芬芳空间识别和艺术场景分类的进一步探索奠定了基础。所有图像和标签以ArtPlaces数据集形式发布，网址为https://zenodo.org/doi/10.5281/zenodo.11584328。
英文摘要：
Olfaction, often overlooked in cultural heritage studies, holds profound significance in shaping human experiences and identities. Examining historical depictions of olfactory scenes can offer valuable insights into the role of smells in history. We show that a transfer-learning approach using weakly labeled training data can remarkably improve the classification of fragrant spaces and, more generally, artistic scene depictions. We fine-tune Places365-pre-trained models by querying two cultural heritage data sources and using the search terms as supervision signal. The models are evaluated on two manually corrected test splits. This work lays a foundation for further exploration of fragrant spaces recognition and artistic scene classification. All images and labels are released as the ArtPlaces dataset at https://zenodo.org/doi/10.5281/zenodo.11584328.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
