用深度学习融合画作图像，提升首次拍卖品估值精度。
Deep Learning for Art Market Valuation
- 构建多模态模型，融合图像与表格数据预测艺术品价格。
- 图像特征对无历史交易记录的新作品估值贡献显著。
- 适合艺术市场研究者与拍卖行从业者参考。
我们研究深度学习如何通过整合艺术品视觉内容提升艺术市场估值能力。基于大型重复销售数据集，对比传统享乐回归与树模型，以及现代多模态深度架构（融合表格与图像数据）。结果表明，尽管艺术家身份和过往交易记录主导整体预测力，但视觉嵌入在缺乏历史参考的首次上市作品中仍提供独特且经济上可解释的贡献。通过Grad-CAM与嵌入可视化分析发现，模型关注构图与风格线索。研究表明，多模态深度学习在估值最困难的首次销售场景中价值突出，为艺术市场研究与实践提供了新洞见。
原文摘要 · Abstract (English)
We study how deep learning can improve valuation in the art market by incorporating the visual content of artworks into predictive models. Using a large repeated-sales dataset from major auction houses, we benchmark classical hedonic regressions and tree-based methods against modern deep architectures, including multi-modal models that fuse tabular and image data. We find that while artist identity and prior transaction history dominate overall predictive power, visual embeddings provide a distinct and economically meaningful contribution for fresh-to-market works where historical anchors are absent. Interpretability analyses using Grad-CAM and embedding visualizations show that models attend to compositional and stylistic cues. Our findings demonstrate that multi-modal deep learning delivers significant value precisely when valuation is hardest, namely first-time sales, and thus offers new insights for both academic research and practice in art market valuation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。