  This paper introduces a new fundamental characteristic, \ie, the dynamic
range, from real-world metric tools to deep visual recognition. In metrology,
the dynamic range is a basic quality of a metric tool, indicating its
flexibility to accommodate various scales. Larger dynamic range offers higher
flexibility. In visual recognition, the multiple scale problem also exist.
Different visual concepts may have different semantic scales. For example,
``Animal'' and ``Plants'' have a large semantic scale while ``Elk'' has a much
smaller one. Under a small semantic scale, two different elks may look quite
\emph{different} to each other . However, under a large semantic scale (\eg,
animals and plants), these two elks should be measured as being \emph{similar}.
%We argue that such flexibility is also important for deep metric learning,
because different visual concepts indeed correspond to different semantic
scales.
  Introducing the dynamic range to deep metric learning, we get a novel
computer vision task, \ie, the Dynamic Metric Learning. It aims to learn a
scalable metric space to accommodate visual concepts across multiple semantic
scales. Based on three types of images, \emph{i.e.}, vehicle, animal and online
products, we construct three datasets for Dynamic Metric Learning. We benchmark
these datasets with popular deep metric learning methods and find Dynamic
Metric Learning to be very challenging. The major difficulty lies in a conflict
between different scales: the discriminative ability under a small scale
usually compromises the discriminative ability under a large one, and vice
versa. As a minor contribution, we propose Cross-Scale Learning (CSL) to
alleviate such conflict. We show that CSL consistently improves the baseline on
all the three datasets. The datasets and the code will be publicly available at
https://github.com/SupetZYK/DynamicMetricLearning.
