The Retrieval of the Beautiful: Self-Supervised Salient Object Detection for Beauty Product Retrieval

Jiawei Wang, Shuai Zhu, Jiao Xu, Da Cao

Abstract: Beauty product retrieval is a challenging task due to the severe image variation issue in real-world scenes. In this work, to mitigate the data variation problem, we contribute a background-agnostic feature extractor, which is trained by a self-supervised salient object detection method. In particular, we first propose a foreground augmentation technique to acquire the augmentation image with its foreground mask. Next, a feature extractor with an attention pooling layer is proposed to learn background-agnostic representations by performing the salient object detection in a self-supervised manner. Finally, we ensemble the background-agnostic features of multiple models to perform the beauty product retrieval. Extensive experimental results have demonstrated the superiority of our proposed framework.