Few-Shot Learning by Exploiting Object Relation

Liangqu Long, Wei Wang, Jun Wen, Meihui Zhang, Qian Lin

Abstract:

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.

Keywords: few-shot learning, relation learning

TL;DR: Few-shot learning by exploiting the object-level relation to learn the image-level relation (similarity)