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

TL;DR

This paper proposes a new deep metric learning approach called Dynamic Metric Learning, which aims to create a scalable metric space accommodating multiple semantic scales in visual recognition, addressing the challenge of conflicting discriminative abilities across scales.

Contribution

It introduces the concept of dynamic range to deep metric learning, constructs three datasets for this task, and proposes Cross-Scale Learning to mitigate conflicts between different semantic scales.

Findings

Dynamic Metric Learning is highly challenging due to scale conflicts.

Cross-Scale Learning improves baseline performance across datasets.

Constructed datasets enable benchmarking of multi-scale visual recognition methods.

Abstract

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.