  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.
