Superclass-aware network for few-shot learning

Humans can learn to recognize a novel object by just going through its images a few times. It might because that they do not recognize the novel object purely on the visual information, but also based on their prior knowledge. Inspired from this, we propose a novel framework named Superclass-aware Network (Sup-Net) to tackle the few-shot learning problem. We first present a knowledge extraction schema in Sup-Net, which can acquire superclass information, and compute superclass semantic relations between different categories. We introduce a novel soft label supervised contrastive loss to help extract discriminative superclass features from images so that the superclass relation can be captured by these features. A novel model architecture that is jointly trained by images and prior knowledge has been proposed. The model encodes image features that minimize the cross-entropy loss at the category level, while it also extracts the superclass feature that minimizes the soft label contrastive loss at the superclass level. Experimental results demonstrate that Sup-Net achieves competitive results on miniImageNet datasets. In addition, we conduct experiments on a large-scale dataset tieredImageNet; the results further demonstrate the effectiveness of our Sup-Net.