Scene Context Enhanced Network for Person Search.

Person search is a practical but challenging task aiming to locate and identify the target person in unconstrained images. Most existing works are focused on people's features while ignores scene features, which can help generate effective features for person identities. To address this issue, we present a Scene Contextual Enhanced Network(SCENet), by introducing attention refined scene contextual features to enhance person embedding. Also, a Scene Person Dissimilarity Loss is proposed to relieve the negative effects of irrelevant people in scene features. Experimental results on two person search benchmarks, i.e., CUHK-SYSU and PRW, demonstrate that our proposed model outperforms existing methods and achieves state-of-the-art performances.