Deep Batch Active Learning and Knowledge Distillation for Person Re-Identification

For deep learning model training, most existing supervised learning-based person re-identification (Re-ID) models require considerable data with annotations as samples. However, it is labor-intensive to generate labeled data in many real-world situations. Meanwhile, the large scale of the existing models increases the load of model learning. To this end, this paper proposes a person Re-ID method by deep batch active learning and knowledge distillation. With the goal of minimizing the human labeling cost and maximizing the performance of the person Re-ID model, a batch active learning algorithm is applied to person Re-ID, which selects samples with both uncertainty and diversity, and the model needs only a small amount of labeled data to achieve a high performance. In addition, a knowledge distillation approach is used to compress the original backbone model to reduce the model scale while maintaining the model performance. Furthermore, experiments on two public datasets demonstrate the effectiveness and superiority of the proposed method.