Fine-grained Classification Algorithm based on Meta-learning

As a challenging computer vision task, fine-grained visual categorization (FGVC) has received more and more attention. Most fine-grained classification algorithms are data-hungry and time-consuming, on the contrary, humans have the ability to recognize different species with only a small number of samples. For example, a baby can identity different kinds of dogs with only few images. Therefore, in this paper, a fined-grained classification model which is based on meta learning is proposed, and it can be used for more accurate fined-grained classification with only a small number of data. Meta learning uses meta-learners to learn relative tasks so that they can handle with new tasks faster and better when facing new tasks. Specifically, our model uses a task learner to learn various tasks and update the parameters of meta learner at the same time. When facing a new task, meta learner can learn and adapt to new tasks more quickly. The key novelty of our model is an attention module which can enlarge the local area response of image. This model has achieved great results on both CUB-200-2011 and Stanford dogs, and the accuracy of this model has achieved 81.74% with only one image of CUB-200-2011.