论文标题
快速时尚的多模式宇宙：Visuelle 2.0基准
The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark
论文作者
论文摘要
我们介绍了Visuelle 2.0，这是第一个数据集，可用于面对快速时尚公司必须经常管理的各种预测问题。此外，我们演示了在这种情况下使用计算机视觉的使用方式。 Visuelle 2.0包含6个季节 / 5355个Nuna Lie服装产品的数据，该公司是一家著名的意大利公司，该公司在该国不同地区拥有数百家商店。特别是，我们专注于一个特定的预测问题，即短暂观察新产品销售预测（So-Fore）。因此，因此假设本赛季已经开始，并且一组新产品在不同商店的货架上。目的是预测特定视野的销售，因为没有较早的统计数据可用。为了取得成功，所谓的方法应捕获这种短暂的过去并利用其他方式或外源数据。为了这些目标，Visuelle 2.0配备了在项目商店级别的分组数据，每个服装项目的多模式信息都可以进行，从而可以发挥计算机视觉方法。我们传达的主要信息是，与竞争性基线方法相比，在长期预测场景中使用时间序列时，使用具有深网的图像数据可以提高表现性能，从而将WAPE和MAE改善高达5.48％和7％。该数据集可从https://humaticslab.github.io/forecasting/visuelle获得
We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE and MAE by up to 5.48% and 7% respectively compared to competitive baseline methods. The dataset is available at https://humaticslab.github.io/forecasting/visuelle
