Fashion knowledge helps people to dress properly and addresses not only physiological needs of users, but also the demands of social activities and conventions. It usually involves three mutually related aspects of: occasion, person and clothing. However, there are few works focusing on extracting such knowledge, which will greatly benefit many downstream applications, such as fashion recommendation. In this paper, we propose a novel method to automatically harvest fashion knowledge from social media. We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. For person detection and analysis, we use the off-the-shelf tools due to their flexibility and satisfactory performance. For clothing recognition and occasion prediction, we unify the two tasks by using a contextualized fashion concept learning module, which captures the dependencies and correlations among different fashion concepts. To alleviate the heavy burden of human annotations, we introduce a weak label modeling module which can effectively exploit machine-labeled data, a complementary of clean data. In experiments, we contribute a benchmark dataset and conduct extensive experiments from both quantitative and qualitative perspectives. The results demonstrate the effectiveness of our model in fashion concept prediction, and the usefulness of extracted knowledge with comprehensive analysis.

本篇论文提出了一种从社交媒体中自动提取时尚知识的方法，该方法结合了图像、文本和元数据多种模态中的场合、人物和服装三个方面进行探测，并采用上下文化时尚概念学习模块提供了对于人物检测和服装识别的解决方案，在机器标注数据有限的情况下，通过弱标签建模模块有效地利用了机器标注数据。实验结果证明了我们的模型在时尚概念预测方面的有效性以及提取知识与全面分析的有用性。

从社交媒体中提取时尚知识：谁、在哪里、穿什么？