Visual fashion analysis has attracted many attentions in the recent years.
Previous work represented clothing regions by either bounding boxes or human
joints. This work presents fashion landmark detection or fashion alignment,
which is to predict the positions of functional key points defined on the
fashion items, such as the corners of neckline, hemline, and cuff. To encourage
future studies, we introduce a fashion landmark dataset with over 120K images,
where each image is labeled with eight landmarks. With this dataset, we study
fashion alignment by cascading multiple convolutional neural networks in three
stages. These stages gradually improve the accuracies of landmark predictions.
Extensive experiments demonstrate the effectiveness of the proposed method, as
well as its generalization ability to pose estimation. Fashion landmark is also
compared to clothing bounding boxes and human joints in two applications,
fashion attribute prediction and clothes retrieval, showing that fashion
landmark is a more discriminative representation to understand fashion images.

本文提出了一种时装关键点检测技术，结合卷积神经网络算法并使用时装关键点数据集，在时尚属性预测和服装检索等应用中证明了其有效性和较强的区分度。