Fashion knowledge encourages people to properly dress and faces not only physiological necessity of users, but also the requirement of social practices and activities. It usually includes three jointly related aspects of: occasion, person and clothing. Nowadays, social media platforms allow users to interact with each other online to share opinions and information. The use of social media sites such as Instagram has already spread to almost every fashion brand and been evaluated as business take-off tools. With the heightened use of social media as a means of marketing communication for fashion brands, it has become necessary to empirically analyse and extract fashion knowledge from them. Thus, social brands are investing on them. In this way, they can understand the consumer’s preferences. This change is also having a significant impact on social media data analysis. To solve this issue, the Deep learning (DL) methods are proven to be effective solutions due to their automatic learning capability. However, little systematic work currently exists on how researchers have applied DL for analysing fashion knowledge from social media data. Hence, this contribution outlines DL-based techniques for social media data related to fashion domain. In this study, a review of the dataset within the fashion world and the DL methods applied on, it is presented to help out new researchers interested in this subject. In particular, five different tasks will be considered: Object Detection, that includes Clothes Landmark Detection, Clothes Parsing and Product Retrieval, Fashion Classification, Clothes Generation, Automatic Fashion Knowledge Extraction and Clothes Recommendation. Therefore, the purpose of this paper is to underline the multiple applications within the fashion world using deep learning techniques. However, this review does not cover all the methods used: in fact, only Deep Learning methods have been analyzed. This choice was made since, given the huge amount of fashion social media data that has been collected, Deep Learning methods achieve best performance both in terms of efficiency and time. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.

In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are—the three “Ideal Cities” (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the “Studiolo del Duca” with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than $80\%$), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums’ visitors.