Abstract

Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, global embedding learned by naive classification model tends to be trapped in the most discriminative local region, leading to poor evaluation performance. To address the issue, we propose a novel baseline network that learns strong global feature termed as Comprehensive Global Embedding (CGE), ensuring more local regions of global feature maps to be discriminative. In this work, two key modules are proposed including Non-parameterized Local Classifier (NLC) and Global Logits Revise (GLR). The NLC is designed to obtain a score vector of each local region on feature maps in a non-parametric manner. The GLR module directly revises the logits such that the subsequent cross entropy loss up-weights the loss assigned to samples with hard-to-learn local regions. The convergence of the deep model indicates more local regions (the number of local regions is manually defined) on the feature maps of each sample are discriminative. We implement these two modules on two strong baseline methods including the BagTricks (BOT) [1] and AGW [2]. The network achieves 65.9% mAP, 85.1% rank1 on MSMT17, 86.4% mAP, 87.4% rank1 on CUHK03 labeled, 84.2% mAP, 85.9% rank1 on CUHK03 detected, and 92.2% mAP, 96.3% rank1 on Market-1501. The results show that the proposed baseline achieves a new state-of-the-art when using only global embedding during inference without any re-ranking technique.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.