Multi-level Feature Aggregation Network for Person Re-identification

Abstract

Current person re-identification works focus on the deep features, ignoring the loss of detailed features due to downsampling operations. We propose a parallel structured Multi-level Feature Aggregation network (MFA-Net), which not only aggregates the information of local features within the same level as that of global features but also aggregates the features between different levels in an orderly manner as a way to mine the overlooked features. Furthermore, experiments on three datasets Market1501, DukeMTMC-RelD and MSMT17, show that our MFA-Net can further mine the detailed features, enhance the feature representation, and achieve state-of-the-art results on three benchmark datasets.