  Recent advances in multi-modal vision and language tasks enable a new set of
applications. In this paper, we consider the task of generating natural
language fashion feedback on outfit images. We collect a unique dataset, which
contains outfit images and corresponding positive and constructive fashion
feedback. We treat each feedback type separately, and train deep generative
encoder-decoder models with visual attention, similar to the standard image
captioning pipeline. Following this approach, the generated sentences tend to
be too general and non-informative. We propose an alternative decoding
technique based on the Maximum Mutual Information objective function, which
leads to more diverse and detailed responses. We evaluate our model with common
language metrics, and also show human evaluation results. This technology is
applied within the ``Alexa, how do I look?'' feature, publicly available in
Echo Look devices.
