Cutmix Dual Branch Network for Person Re-Identification
Zengming Tang, Jun Huang · 2021
The performance of deep learning methods for person re-identification (Re-ID) is influenced by overfitting problem. To improve the generalization ability, most methods pay attention to generating and utilizing new samples. However, generated samples focus on object occlusion but neglect pedestrian occlusion, while triplet loss fails to preserve all identity information on samples with multiple pedestrians. In this paper, we propose the CutMix dual branch network (CDBN) to relieve these problems. CutMix is introduced to this model, and it is responsible for generating new samples with pedestrian occlusion. CutMix is verified complementary to image erasing strategy for Re-ID. Besides, a generalized triplet loss called CutMix triplet loss (CTP) is employed on samples augmented by CutMix with consideration of identity information from multiple pedestrians, making CDBN robust to two kinds of occlusions. Extensive experiments on two bench-marks demonstrate the strengths of CDBN, which is superior to state-of-the-art methods.