Margin Constraint for Low-Shot Learning

Low-shot learning aims to recognize novel visual categories with limited examples, which is mimicking the human visual system and remains a challenging research problem. In this paper, we introduce the margin constraint in loss function for the low-shot learning field to enhance the model’s discriminative power. Additionally, we adopt the novel categories’ normalized feature vectors as the corresponding classification weight vectors directly, in order to provide an instant classification performance on the novel categories without retraining. Experiments show that our method provides a better generalization and outperforms the previous methods on the low-shot leaning benchmarks.