  Low-shot learning indicates the ability to recognize unseen objects based on
very limited labeled training samples, which simulates human visual
intelligence. According to this concept, we propose a multi-level similarity
model (MLSM) to capture the deep encoded distance metric between the support
and query samples. Our approach is achieved based on the fact that the image
similarity learning can be decomposed into image-level, global-level, and
object-level. Once the similarity function is established, MLSM will be able to
classify images for unseen classes by computing the similarity scores between a
limited number of labeled samples and the target images. Furthermore, we
conduct 5-way experiments with both 1-shot and 5-shot setting on Caltech-UCSD
datasets. It is demonstrated that the proposed model can achieve promising
results compared with the existing methods in practical applications.
