  Recognising detailed clothing characteristics (fine-grained attributes) in
unconstrained images of people in-the-wild is a challenging task for computer
vision, especially when there is only limited training data from the wild
whilst most data available for model learning are captured in well-controlled
environments using fashion models (well lit, no background clutter, frontal
view, high-resolution). In this work, we develop a deep learning framework
capable of model transfer learning from well-controlled shop clothing images
collected from web retailers to in-the-wild images from the street.
Specifically, we formulate a novel Multi-Task Curriculum Transfer (MTCT) deep
learning method to explore multiple sources of different types of web
annotations with multi-labelled fine-grained attributes. Our multi-task loss
function is designed to extract more discriminative representations in training
by jointly learning all attributes, and our curriculum strategy exploits the
staged easy-to-complex transfer learning motivated by cognitive studies. We
demonstrate the advantages of the MTCT model over the state-of-the-art methods
on the X-Domain benchmark, a large scale clothing attribute dataset. Moreover,
we show that the MTCT model has a notable advantage over contemporary models
when the training data size is small.
