Fashion Analytics Based on Deep Learning Visual Processing

For instructions on how to apply, please see: PhD Studentships: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents.

Supervisors:

• Marco Cristani: Humatics Srl
• Alessandro Vinciarelli: School of Computing Science

Understanding and anticipating future trends is crucial for fashion companies looking to maximise their profit. Many machine learning approaches have been devoted to fashion forecasting, all of them with a strong limitation: they model fashion styles as sets of textual attributes; for example, “dotted t-shirt with skinny jeans” defines an outfit which may correspond to many real outfits, since it misses the color, the size of the dots, the type of the neckline, etc. Actually, the description does not incorporate the crucial part: the appearance. A picture is worth a thousand words, especially when it comes to fashion, where subtle, fine-grained variations of a pattern may define a style. Few instants are needed to distinguish a female outfit of 1920 from one of the last years, but both of them have the same textual description: “Below-knee length drop-waist dresses with a loose, straight fit” describes a 1920 style; when copy-pasted to Google, it brings you to Zalando’s contemporary products! The devil is in the detail, and this detail is visual, and cannot described by words. With this PhD project, we want to model fashion exploiting visual patterns, as they were letters of a new artistic vocabulary, within deep network architectures. Deep learning allows to map complicate patterns into a mathematical space, including images, without the need to use words. In this space, similarities can be computed, which are way more effective then written descriptions, clearly differentiating the last trends from the ones of a century ago. Deep learning is particularly effective when many data are used. And fashion, nowadays, comes together with social media, where tons of images are now the new oil of communication, presenting clothing items with pictures and video, with a pace of hundreds of thousands of items each day. This is the scenario where we will locate: our PhD theme will deal with fashion images collected on social media, in order to give deep learning the capability of perfectly understanding a style. Finally, our PhD will aim at forecasting fashion trends, in order to predict the rise and fall of a particular visual trend. This will be possible by social signal processing, which treats the images together with the “likes” associated to them, predicting when an image of a clothing will become viral, understanding among all of the images the ones which are more important than the others in defining a trend, his rise and fall. The PhD theme will put the student in contact with Humatics, a young Italian start-up, which is currently working with important fast-fashion companies as Nunalie , Sirmoney, furnishing forecasting services, and looking to international collaborations to improve their services, and to create specialized professional in the field of computational fashion and aesthetics.