Modeling Fashion Compatibility with Explanation by using Bidirectional LSTM

The goal of this paper is to model the fashion compatibility of an outfit and provide the explanations. We first extract features of all attributes of all items via convolutional neural networks, and then train the bidirectional Long Shortterm Memory (Bi-LSTM) model to learn the compatibility of an outfit by treating these attribute features as a sequence. Gradient penalty regularization is exploited for training inter-factor compatibility net which is used to compute the loss for judgment and provide its explanation which is generated from the recognized reasons related to the judgment. To train and evaluate the proposed approach, we expanded the EVALUATION3 dataset in terms of the number of items and attributes. Experiment results show that our approach can successfully evaluate compatibility with reason.

Automated summary

The paper aims to model the fashion compatibility of an outfit and provide explanations using convolutional neural networks and Bi-LSTM model. Experimental results demonstrate the success of the proposed approach in evaluating compatibility and reasons behind it.