Person Re-Identification With Reinforced Attribute Attention Selection
Jianfu Zhang, Li Niu, Liqing Zhang
Abstract
Person re-identification (Re-ID) aims to match pedestrian images across various scenes in video surveillance. There are a few works using attribute information to boost Re-ID performance. Specifically, those methods leverage attribute information to boost Re-ID performance by introducing auxiliary tasks like verifying the image level attribute information of two pedestrian images or recognizing identity level attributes. Identity level attribute annotations cost less manpower and are well-fitted for person re-identification task compared with image-level attribute annotations. However, the identity attribute information may be very noisy due to incorrect attribute annotation or lack of discriminativeness to distinguish different persons, which is probably unhelpful for the Re-ID task. In this paper, we propose a novel Attribute Attentional Block (AAB), which can be integrated into any backbone network or framework. Our AAB adopts reinforcement learning to drop noisy attributes based on our designed reward and then utilizes aggregated attribute attention of the remaining attributes to facilitate the Re-ID task. Experimental results demonstrate that our proposed method achieves state-of-the-art results on three benchmark datasets.