Towards Predicting the Likeability of Fashion Images
This addresses the problem of predicting fashion image popularity for users and platforms, but it is incremental as it builds on existing attribute and ranking techniques.
The paper tackles the problem of ranking fashion images by likeability using new datasets from Pinterest and Polyvore, achieving effectiveness through a method that combines semantic and data-driven attributes with a ranking SPN.
In this paper, we propose a method for ranking fashion images to find the ones which might be liked by more people. We collect two new datasets from image sharing websites (Pinterest and Polyvore). We represent fashion images based on attributes: semantic attributes and data-driven attributes. To learn semantic attributes from limited training data, we use an algorithm on multi-task convolutional neural networks to share visual knowledge among different semantic attribute categories. To discover data-driven attributes unsupervisedly, we propose an algorithm to simultaneously discover visual clusters and learn fashion-specific feature representations. Given attributes as representations, we propose to learn a ranking SPN (sum product networks) to rank pairs of fashion images. The proposed ranking SPN can capture the high-order correlations of the attributes. We show the effectiveness of our method on our two newly collected datasets.