论文标题
使用特权中间信息的可见红外人员重新识别
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information
论文作者
论文摘要
可见的红外人员重新识别（REID）旨在认识到RGB和IR摄像机网络中同一感兴趣的人。一些深度学习（DL）模型已直接纳入了两种模式，以在联合表示空间中区分人。但是，由于RGB和IR模式之间数据分布的较大域变化，因此这个跨模式的REID问题仍然具有挑战性。 ％ 本文介绍了一种新的方法，用于创建中间虚拟领域，该域在训练过程中充当两个主要领域（即RGB和IR模式）之间的桥梁。该中间域被视为在测试时无法获得的特权信息（PI），并允许将此跨模式匹配任务制定为在特权信息（LUPI）下学习的问题。我们设计了一种新方法，以在可见的和红外域之间生成图像，这些方法提供了其他信息，可以通过中间域的适应来训练深层REID模型。特别是，通过在训练过程中采用无色和多步三重态损失目标，我们的方法提供了通用的特征表示空间，这些空间可对大型可见的红外域移动稳健。 ％ 有关挑战可见的红外REID数据集的实验结果表明，我们提出的方法始终提高匹配的精度，而在测试时没有任何计算开销。该代码可在：\ href {https://github.com/alehdaghi/cross-modal-re-iid-id-via-lupi} {https://github.com/alehdaghi/alehdaghi/cross-modal-re-re-id-iid-via-lupi} {https://github.com/
Visible-infrared person re-identification (ReID) aims to recognize a same person of interest across a network of RGB and IR cameras. Some deep learning (DL) models have directly incorporated both modalities to discriminate persons in a joint representation space. However, this cross-modal ReID problem remains challenging due to the large domain shift in data distributions between RGB and IR modalities. % This paper introduces a novel approach for a creating intermediate virtual domain that acts as bridges between the two main domains (i.e., RGB and IR modalities) during training. This intermediate domain is considered as privileged information (PI) that is unavailable at test time, and allows formulating this cross-modal matching task as a problem in learning under privileged information (LUPI). We devised a new method to generate images between visible and infrared domains that provide additional information to train a deep ReID model through an intermediate domain adaptation. In particular, by employing color-free and multi-step triplet loss objectives during training, our method provides common feature representation spaces that are robust to large visible-infrared domain shifts. % Experimental results on challenging visible-infrared ReID datasets indicate that our proposed approach consistently improves matching accuracy, without any computational overhead at test time. The code is available at: \href{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI}{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI}
