Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes

Abstract

Popular fashion e-commerce platforms mostly provide details about low-level attributes of an apparel (for example, neck type, dress length, collar type, print etc) on their product detail pages. However, customers usually prefer to buy apparel based on their style information, or simply put, occasion (for example, party wear, sports wear, casual wear etc). Application of a supervised image-captioning model to generate style-based image captions is limited because obtaining ground-truth annotations in the form of style-based captions is difficult. This is because annotating style-based captions requires a certain amount of fashion domain expertise, and also adds to the costs and manual effort. On the contrary, low-level attribute based annotations are much more easily available. To address this issue, we propose a transfer-learning based image captioning model that is trained on a source dataset with sufficient attribute-based ground-truth captions, and used to predict style-based captions on a target dataset. The target dataset has only a limited amount of images with style-based ground-truth captions. The main motivation of our approach comes from the fact that most often there are correlations among the low-level attributes and the higher-level styles for an apparel. We leverage this fact and train our model in an encoder-decoder based framework using attention mechanism. In particular, the encoder of the model is first trained on the source dataset to obtain latent representations capturing the low-level attributes. The trained model is fine-tuned to generate style-based captions for the target dataset. To highlight the effectiveness of our method, we qualitatively and quantitatively demonstrate that the captions generated by our approach are close to the actual style information for the evaluated apparel. A Proof Of Concept (POC) for our model is under pilot at Myntra (www.myntra.com) where it is exposed to some internal users for feedback.