  Feature matters. How to train a deep network to acquire discriminative
features across categories and polymerized features within classes has always
been at the core of many computer vision tasks, specially for large-scale
recognition systems where test identities are unseen during training and the
number of classes could be at million scale. In this paper, we address this
problem based on the simple intuition that the cosine distance of features in
high-dimensional space should be close enough within one class and far away
across categories. To this end, we proposed the congenerous cosine (COCO)
algorithm to simultaneously optimize the cosine similarity among data. It
inherits the softmax property to make inter-class features discriminative as
well as shares the idea of class centroid in metric learning. Unlike previous
work where the center is a temporal, statistical variable within one mini-batch
during training, the formulated centroid is responsible for clustering
inner-class features to enforce them polymerized around the network truncus.
COCO is bundled with discriminative training and learned end-to-end with stable
convergence. Experiments on five benchmarks have been extensively conducted to
verify the effectiveness of our approach on both small-scale classification
task and large-scale human recognition problem.
