  Popular fashion e-commerce platforms mostly provide details about low-level
attributes of an apparel (eg, neck type, dress length, collar type) on their
product detail pages. However, customers usually prefer to buy apparel based on
their style information, or simply put, occasion (eg, party/ sports/ casual
wear). 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 for our model is under pilot at Myntra where it is exposed to
some internal users for feedback.
