  Estimating the preferences of consumers is of utmost importance for the
fashion industry as appropriately leveraging this information can be beneficial
in terms of profit. Trend detection in fashion is a challenging task due to the
fast pace of change in the fashion industry. Moreover, forecasting the visual
popularity of new garment designs is even more demanding due to lack of
historical data. To this end, we propose MuQAR, a Multimodal
Quasi-AutoRegressive deep learning architecture that combines two modules: (1)
a multi-modal multi-layer perceptron processing categorical, visual and textual
features of the product and (2) a quasi-autoregressive neural network modelling
the "target" time series of the product's attributes along with the "exogenous"
time series of all other attributes. We utilize computer vision, image
classification and image captioning, for automatically extracting visual
features and textual descriptions from the images of new products. Product
design in fashion is initially expressed visually and these features represent
the products' unique characteristics without interfering with the creative
process of its designers by requiring additional inputs (e.g manually written
texts). We employ the product's target attributes time series as a proxy of
temporal popularity patterns, mitigating the lack of historical data, while
exogenous time series help capture trends among interrelated attributes. We
perform an extensive ablation analysis on two large scale image fashion
datasets, Mallzee and SHIFT15m to assess the adequacy of MuQAR and also use the
Amazon Reviews: Home and Kitchen dataset to assess generalisability to other
domains. A comparative study on the VISUELLE dataset, shows that MuQAR is
capable of competing and surpassing the domain's current state of the art by
4.65% and 4.8% in terms of WAPE and MAE respectively.
