感知关注点：学习可见性感知的部分级别特征用于部分人员重新识别
Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification
摘要：
本文考虑了人再识别（re-ID）任务中的一个现实问题，即，在部分re-ID场景下，图像可以包含行人的部分观察。如果我们直接将部分行人图像与整体图像进行比较，极端的空间不对准会显着损害学习表示的区分能力。我们提出了一个可见性感知的部分模型（VPM），它学习感知区域的可见性，通过自我监督。可见性感知允许VPM提取区域级别的特征并比较两个图像，重点是它们的共享区域（在两个图像上都可见）。VPM获得了两方面的好处，提高了部分重新识别的准确性。一方面，与学习全局特征相比，VPM学习区域级特征并受益于细粒度信息。另一方面，与可见性意识，VPM是能够估计两个图像之间的共享区域，从而抑制空间错位。实验结果证实，我们的方法显着提高了学习的表示和实现的准确性是与最先进的水平。
英文摘要：
This paper considers a realistic problem in person re-identification (re-ID) task, i.e., partial re-ID. Under partial re-ID scenario, the images may contain a partial observation of a pedestrian. If we directly compare a partial pedestrian image with a holistic one, the extreme spatial misalignment significantly compromises the discriminative ability of the learned representation. We propose a Visibility-aware Part Model (VPM), which learns to perceive the visibility of regions through self-supervision. The visibility awareness allows VPM to extract region-level features and compare two images with focus on their shared regions (which are visible on both images). VPM gains two-fold benefit toward higher accuracy for partial re-ID. On the one hand, compared with learning a global feature, VPM learns region-level features and benefits from fine-grained information. On the other hand, with visibility awareness, VPM is capable to estimate the shared regions between two images and thus suppresses the spatial misalignment. Experimental results confirm that our method significantly improves the learned representation and the achieved accuracy is on par with the state of the art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
