范畴变量
计算机科学
绘画
人工智能
估价(财务)
图像(数学)
深度学习
机器学习
艺术
视觉艺术
经济
财务
作者
Jason Orlando Indrawan,Beatrice Josephine Filia,Chyntia,Ivan Halim Parmonangan,. Diana
标识
DOI:10.1109/icoris60118.2023.10352208
摘要
The art market's subjective and unpredictable nature has led to a growing interest in predicting the prices of paintings using machine learning models. This study aims to explore a multimodal approach combining CNN, BERT, and regular NN to predict painting prices based on diverse inputs, including image, text, and categorical data. The dataset was collected from Sotheby's art marketplace website, consisting of various features such as image, dimensions, movement, period, material, condition, and price. Results from the model evaluation show that the multimodal approach outperformed single-modal models, achieving a MAPE loss of 30.66% and an MAE loss of 3348.73. The integration of numerical, categorical, image, and text data enhanced the model's ability to capture the complexities involved in painting valuation. The positive impact of the multimodal approach lies in its ability to enhance prediction accuracy and capture the relation of many factors affecting painting prices, ultimately providing valuable insights and tools for the art market.
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