Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

This paper introduces a new fundamental characteristic, i.e., 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 different to each other . However, under a large semantic scale (e.g., animals and plants), these two elks should be measured as being similar.