Abstract
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.
Abstract (translated)
本文考虑了人身识别（RE-ID）任务中的一个现实问题，即部分RE-ID，在部分RE-ID场景下，图像可能包含对行人的部分观察。如果我们直接将部分行人图像与整体图像进行比较，那么极端的空间错位会严重损害所学表示的识别能力。我们提出了一个可见性感知部分模型（VPM），通过自我监督学习感知区域的可见性。可见性感知允许VPM提取区域级别的特征，比较两个图像，并将焦点集中在它们的共享区域上（这两个图像都可见）。VPM在提高局部RE-ID的准确性方面获得了两倍的好处。一方面，与学习全局特性相比，VPM学习区域级特性，并从细粒度信息中获益。另一方面，通过视觉感知，VPM能够估计两幅图像之间的共享区域，从而抑制空间偏差。实验结果表明，该方法显著提高了学习表示的精度，达到了与现有技术相当的精度。
URL
https://arxiv.org/abs/1904.00537
