In visible-infrared video person re-identification (re-ID), extracting
features not affected by complex scenes (such as modality, camera views,
pedestrian pose, background, etc.) changes, and mining and utilizing motion
information are the keys to solving cross-modal pedestrian identity matching.
To this end, the paper proposes a new visible-infrared video person re-ID
method from a novel perspective, i.e., adversarial self-attack defense and
spatial-temporal relation mining. In this work, the changes of views, posture,
background and modal discrepancy are considered as the main factors that cause
the perturbations of person identity features. Such interference information
contained in the training samples is used as an adversarial perturbation. It
performs adversarial attacks on the re-ID model during the training to make the
model more robust to these unfavorable factors. The attack from the adversarial
perturbation is introduced by activating the interference information contained
in the input samples without generating adversarial samples, and it can be thus
called adversarial self-attack. This design allows adversarial attack and
defense to be integrated into one framework. This paper further proposes a
spatial-temporal information-guided feature representation network to use the
information in video sequences. The network cannot only extract the information
contained in the video-frame sequences but also use the relation of the local
information in space to guide the network to extract more robust features. The
proposed method exhibits compelling performance on large-scale cross-modality
video datasets. The source code of the proposed method will be released at
this https URL

本文提出一种新的可见 - 红外视频人物再识别方法，从对抗性自攻击防御和时空关系挖掘的新角度解决了跨模态行人身份匹配的问题。同时，通过激活输入样本中包含的干扰信息来引入对抗性干扰，将对抗攻击和防御集成到一个框架中，并提出了一个以空间 - 时间信息引导的特征表示网络来提取更加稳健的特征。在大规模跨模态视频数据集上，该方法表现出了很好的性能。