  We present a novel hierarchical triplet loss (HTL) capable of automatically
collecting informative training samples (triplets) via a defined hierarchical
tree that encodes global context information. This allows us to cope with the
main limitation of random sampling in training a conventional triplet loss,
which is a central issue for deep metric learning. Our main contributions are
two-fold. (i) we construct a hierarchical class-level tree where neighboring
classes are merged recursively. The hierarchical structure naturally captures
the intrinsic data distribution over the whole database. (ii) we formulate the
problem of triplet collection by introducing a new violate margin, which is
computed dynamically based on the designed hierarchical tree. This allows it to
automatically select meaningful hard samples with the guide of global context.
It encourages the model to learn more discriminative features from visual
similar classes, leading to faster convergence and better performance. Our
method is evaluated on the tasks of image retrieval and face recognition, where
it outperforms the standard triplet loss substantially by 1%-18%. It achieves
new state-of-the-art performance on a number of benchmarks, with much fewer
learning iterations.
