  Recent works in the person re-identification task mainly focus on the model
accuracy while ignore factors related to the efficiency, e.g. model size and
latency, which are critical for practical application. In this paper, we
propose a novel Hierarchical and Efficient Network (HENet) that learns
hierarchical global, partial, and recovery features ensemble under the
supervision of multiple loss combinations. To further improve the robustness
against the irregular occlusion, we propose a new dataset augmentation
approach, dubbed Random Polygon Erasing (RPE), to random erase irregular area
of the input image for imitating the body part missing. We also propose an
Efficiency Score (ES) metric to evaluate the model efficiency. Extensive
experiments on Market1501, DukeMTMC-ReID, and CUHK03 datasets shows the
efficiency and superiority of our approach compared with epoch-making methods.
