ArXiv | 2019
Progressive Fashion Attribute Extraction
Abstract
Extracting fashion attributes from images of people wearing clothing/fashion accessories is a very hard multi-class classification problem. Most often, even catalogues of fashion do not have all the fine-grained attributes tagged due to prohibitive cost of annotation. Using images of fashion articles, running multi-class attribute extraction with a single model for all kinds of attributes (neck design detailing, sleeves detailing, etc) requires classifiers that are robust to missing and ambiguously labelled data. In this work, we propose a progressive training approach for such multi-class classification, where weights learnt from an attribute are fine tuned for another attribute of the same fashion article (say, dresses). We branch networks for each attributes from a base network progressively during training. While it may have many labels, an image doesn t need to have all possible labels for fashion articles present in it. We also compare our approach to multi-label classification, and demonstrate improvements over overall classification accuracies using our approach.