SIF: Self-Inspirited Feature Learning for Person Re-Identification

The re-identification (ReID) task has received increasing studies in recent years and its performance has gained significant improvement. The progress mainly comes from searching for new network structures to learn person representations. However, limited efforts have been made to explore the potential performance of existing ReID networks directly by better training scheme, which leaves a large space for ReID research. In this paper, we propose a Self-Inspirited Feature Learning (SIF) method to enhance the performance of given ReID networks from the viewpoint of optimization. We design a simple adversarial learning scheme to encourage a network to learn more discriminative person representation. In our method, an auxiliary branch is added into the network only in the training stage, while the structure of the original network stays unchanged during the testing stage. In summary, SIF has three aspects of advantages: 1) it is designed under general setting; 2) it is compatible with many existing feature learning networks on the ReID task; 3) it is easy to implement and has steady performance. We evaluate the performance of SIF on three public ReID datasets: Market1501, DuckMTMC-reID, and CUHK03(both labeled and detected). The results demonstrate significant improvement in performance brought by SIF. We also apply SIF to obtain state-of-the-art results on all the three datasets. Specifically, mAP / Rank-1 accuracy are: 87.6%/95.2% (without re-rank) on Market1501, 79.4%/89.8% on DuckMTMC-reID, 77.0%/79.5% on CUHK03 (labeled) and 73.9%/76.6% on CUHK03 (detected), respectively.