
Few-shot learning trains image classifiers over datasets with few examples per category. 
 It poses challenges for the optimization algorithms, which typically require many examples to fine-tune the model parameters for new categories. 
 Distance-learning-based approaches avoid the optimization issue by embedding the images into a metric space and applying the nearest neighbor classifier for new categories. In this paper, we propose to exploit the object-level relation to learn the image relation feature, which is converted into a distance directly.
 For a new category, even though its images are not seen by the model, some objects may appear in the training images. Hence, object-level relation is useful for inferring the relation of images from unseen categories. Consequently, our model generalizes well for new categories without fine-tuning.
 Experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods.
Few-shot learning by exploiting the object-level relation to learn the image-level relation (similarity) This paper deals with the problem of few-shot learning by proposing an embedding-based approach that learns to compare object-level features between support and query set examples Proposes a few shot learning method that exploits the object-level relation between different images based on neared neighbor search and concatenates feature maps of two input images into one feature map