RnR: Extraction of Visual Attributes from Large-Scale Fashion Dataset

Supervised visual perception models require a large number of annotated inputs so that a model can be trained and validated. However, manual annotation of large-scale datasets can be prohibitive in terms of time and expense. We present an automatic fashion image tagging algorithm that uses detailed item descriptions and carefully selected category information produced by a service provider. Compared to previous work, which used a relatively simple frequency-based heuristic, a set of image attributes extracted by our proposed algorithm could be used to create a model (ResNet-18 architecture) with exhibited 8% higher accuracy than a model created with attributes suggested by DeepFashion. This in-progress work identifies opportunities that carefully chosen visually recognizable attributes can be used to help build a more general and descriptive model.