Abstract
In this work, we tackle the zero-shot metric learning problem and propose a novel method abbreviated as ZSML, with the purpose to learn a distance metric that measures the similarity of unseen
categories (even unseen datasets). ZSML achieves
strong transferability by capturing multi-nonlinear
yet continuous relation among data. It is motivated by two facts: 1) relations can be essentially
described from various perspectives; and 2) traditional binary supervision is insufficient to represent
continuous visual similarity. Specifically, we first
reformulate a collection of specific-shaped convolutional kernels to combine data pairs and generate
multiple relation vectors. Furthermore, we design
a new cross-update regression loss to discover continuous similarity. Extensive experiments including
intra-dataset transfer and inter-dataset transfer on
four benchmark datasets demonstrate that ZSML
can achieve state-of-the-art performance