Learning Unified Embedding for Apparel Recognition

In apparel recognition, specialized models (e.g. models
trained for a particular vertical like dresses) can signifi-
cantly outperform general models (i.e. models that cover
a wide range of verticals). Therefore, deep neural network
models are often trained separately for different verticals
(e.g. [7]). However, using specialized models for different
verticals is not scalable and expensive to deploy. This paper
addresses the problem of learning one unified embedding
model for multiple object verticals (e.g. all apparel classes)
without sacrificing accuracy. The problem is tackled from
two aspects: training data and training difficulty. On the
training data aspect, we figure out that for a single model
trained with triplet loss, there is an accuracy sweet spot in
terms of how many verticals are trained together. To ease
the training difficulty, a novel learning scheme is proposed
by using the output from specialized models as learning targets
so that L2 loss can be used instead of triplet loss. This
new loss makes the training easier and make it possible for
more efficient use of the feature space. The end result is
a unified model which can achieve the same retrieval accuracy
as a number of separate specialized models, while
having the model complexity as one. The effectiveness of
our approach is shown in experiments.

• Machine Perception