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Person search is a challenging problem with various real-world applications,that aims at joint person detection and re-identification of a query personfrom uncropped gallery images. Although, the previous study focuses on richfeature information learning, it is still hard to retrieve the query person dueto the occurrence of appearance deformations and background distractors. Inthis paper, we propose a novel attention-aware relation mixer (ARM) module forperson search, which exploits the global relation between different localregions within RoI of a person and make it robust against various appearancedeformations and occlusion. The proposed ARM is composed of a relation mixerblock and a spatio-channel attention layer. The relation mixer block introducesa spatially attended spatial mixing and a channel-wise attended channel mixingfor effectively capturing discriminative relation features within an RoI. Thesediscriminative relation features are further enriched by introducing aspatio-channel attention where the foreground and background discriminabilityis empowered in a joint spatio-channel space. Our ARM module is generic and itdoes not rely on fine-grained supervision or topological assumptions, hencebeing easily integrated into any Faster R-CNN based person search methods.Comprehensive experiments are performed on two challenging benchmark datasets:CUHKSYSU and PRW. Our PS-ARM achieves state-of-the-art performance on bothdatasets. On the challenging PRW dataset, our PS-ARM achieves an absolute gainof 5 in the mAP score over SeqNet, while operating at a comparable speed.

PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search