  Analyzing fashion attributes is essential in the fashion design process.
Current fashion forecasting firms, such as WGSN utilizes information from all
around the world (from fashion shows, visual merchandising, blogs, etc). They
gather information by experience, by observation, by media scan, by interviews,
and by exposed to new things. Such information analyzing process is called
abstracting, which recognize similarities or differences across all the
garments and collections. In fact, such abstraction ability is useful in many
fashion careers with different purposes. Fashion forecasters abstract across
design collections and across time to identify fashion change and directions;
designers, product developers and buyers abstract across a group of garments
and collections to develop a cohesive and visually appeal lines; sales and
marketing executives abstract across product line each season to recognize
selling points; fashion journalist and bloggers abstract across runway photos
to recognize symbolic core concepts that can be translated into editorial
features. Fashion attributes analysis for such fashion insiders requires much
detailed and in-depth attributes annotation than that for consumers, and
requires inference on multiple domains. In this project, we propose a
data-driven approach for recognizing fashion attributes. Specifically, a
modified version of Faster R-CNN model is trained on images from a large-scale
localization dataset with 594 fine-grained attributes under different
scenarios, for example in online stores and street snapshots. This model will
then be used to detect garment items and classify clothing attributes for
runway photos and fashion illustrations.
