论文标题
房地产属性从多种视觉方式的预测，缺少数据
Real Estate Attribute Prediction from Multiple Visual Modalities with Missing Data
论文作者
论文摘要
房地产的评估和估值需要大型数据集，其中包含房地产信息。不幸的是，房地产数据库通常在实践中稀疏，即，每个属性都可以使用每个重要属性。在本文中，我们研究了从视觉数据中预测高级房地产属性的潜力，特别是从两个视觉方式中，即室内（内部）和室外（立面）照片。我们使用不同的多模式融合策略设计了三种模型，并在三种不同用例中对其进行了评估。因此，一个特殊的挑战是处理缺失的方式。我们评估了不同的融合策略，目前的基线用于不同的预测任务，并发现使用其他不完整样本丰富训练数据可以提高预测准确性。此外，室内和室外照片的信息融合会导致宏F1得分的性能提高高达5％。
The assessment and valuation of real estate requires large datasets with real estate information. Unfortunately, real estate databases are usually sparse in practice, i.e., not for each property every important attribute is available. In this paper, we study the potential of predicting high-level real estate attributes from visual data, specifically from two visual modalities, namely indoor (interior) and outdoor (facade) photos. We design three models using different multimodal fusion strategies and evaluate them for three different use cases. Thereby, a particular challenge is to handle missing modalities. We evaluate different fusion strategies, present baselines for the different prediction tasks, and find that enriching the training data with additional incomplete samples can lead to an improvement in prediction accuracy. Furthermore, the fusion of information from indoor and outdoor photos results in a performance boost of up to 5% in Macro F1-score.
