Multi-label learning with the RNNs for Fashion Search

Taewan Kim

Nov 05, 2016 (modified: Nov 19, 2016) ICLR 2017 conference submission readers: everyone
  • Abstract: We build a large-scale visual search system which finds similar product images given a fashion item. Defining similarity among arbitrary fashion-products is still remains a challenging problem, even there is no exact ground-truth. To resolve this problem, we define more than 90 fashion-related attributes, and combination of these attributes can represent thousands of unique fashion-styles. We then introduce to use the recurrent neural networks (RNNs) recognising multi fashion-attributes with the end-to-end manner. To build our system at scale, these fashion-attributes are again used to build an inverted indexing scheme. In addition to these fashion-attributes for semantic similarity, we extract colour and appearance features in a region-of-interest (ROI) of a fashion item for visual similarity. By sharing our approach, we expect active discussion on that how to apply current deep learning researches into the e-commerce industry.
  • TL;DR: Works for applying LSTM into the multi-label learning in an application to computer vision
  • Conflicts: sk.com, navercorp.com
  • Keywords: Computer vision, Deep learning, Supervised Learning, Applications

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