  Learning to recognize novel visual categories from a few examples is a
challenging task for machines in real-world industrial applications. In
contrast, humans have the ability to discriminate even similar objects with
little supervision. This paper attempts to address the few shot fine-grained
image classification problem. We propose a feature fusion model to explore
discriminative features by focusing on key regions. The model utilizes the
focus area location mechanism to discover the perceptually similar regions
among objects. High-order integration is employed to capture the interaction
information among intra-parts. We also design a Center Neighbor Loss to form
robust embedding space distributions. Furthermore, we build a typical
fine-grained and few-shot learning dataset miniPPlankton from the real-world
application in the area of marine ecological environments. Extensive
experiments are carried out to validate the performance of our method. The
results demonstrate that our model achieves competitive performance compared
with state-of-the-art models. Our work is a valuable complement to the model
domain-specific industrial applications.
