Part-Relation-Aware Feature Fusion Network for Person Re-Identification

The research of part-based methods has been proven as an effective way in person re-identification (Re-ID) task. However, in existing part-based Re-ID methods, the informative interactions and potential associations among parts are neglected, which demands further study. To fill this gap, we propose a novel Part-relation-aware Feature Fusion Network (PFFN) which achieves a part-level feature fusion and enhances the discrimination of part features by fully employing helpful information from associations among parts. More specifically, a Dual-stage Attention (DA) module, consisting of spatial and part-based channel attention, is proposed to exploit complementary benefits of two kinds of attention information, thereby facilitating model with learning more discriminative features. Furthermore, Part-relation Exploitation (PE) module is proposed to learn relation-aware part features where correlative information among parts are fully employed, thereby bringing a noticeable improvement in performance. Extensive experiments are conducted on four mainstream Re-ID datasets to verify the superiority of PFFN. Compared with baseline model, PFFN has gained rank-1 accuracy improvement of 18.2% on MSMT17-v2, 12.1% on the CUHK03-Labeled, 5.6% on DukeMTMC-reid and 1.3% on Market1501, compellingly validating its effectiveness.