Hierarchical distillation learning for scalable person search

Highlights：

• We investigate for the first time the scalability problem involved in person search. This is a fundamentally significant problem to be solved for scaling up the deep learning solutions to person search in the real-world applications.

• We formulate a hierarchical distillation learning (HDL) approach for more discriminating knowledge transfer from a stronger teacher model into an efficient student model.

• We design a simple and effective teacher model for joint learning of person search, which largely facilitates the knowledge distillation by avoiding knowledge transfer between structure inconsistent teacher and student models.

• We demonstrate the model cost-effectiveness and performance advantages of our HDL over the state-of-the-art alternative approaches on three person search benchmarks.