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Generating accurate descriptions for online fashion items is important notonly for enhancing customers' shopping experiences, but also for the increaseof online sales. Besides the need of correctly presenting the attributes ofitems, the expressions in an enchanting style could better attract customerinterests. The goal of this work is to develop a novel learning framework foraccurate and expressive fashion captioning. Different from popular work onimage captioning, it is hard to identify and describe the rich attributes offashion items. We seed the description of an item by first identifying itsattributes, and introduce attribute-level semantic (ALS) reward andsentence-level semantic (SLS) reward as metrics to improve the quality of textdescriptions. We further integrate the training of our model with maximumlikelihood estimation (MLE), attribute embedding, and Reinforcement Learning(RL). To facilitate the learning, we build a new FAshion CAptioning Dataset(FACAD), which contains 993K images and 130K corresponding enchanting anddiverse descriptions. Experiments on FACAD demonstrate the effectiveness of ourmodel.

Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards