In many scenarios of Person Re-identification (Re-ID), the gallery set
consists of lots of surveillance videos and the query is just an image, thus
Re-ID has to be conducted between image and videos. Compared with videos, still
person images lack temporal information. Besides, the information asymmetry
between image and video features increases the difficulty in matching images
and videos. To solve this problem, we propose a novel Temporal Knowledge
Propagation (TKP) method which propagates the temporal knowledge learned by the
video representation network to the image representation network. Specifically,
given the input videos, we enforce the image representation network to fit the
outputs of video representation network in a shared feature space. With back
propagation, temporal knowledge can be transferred to enhance the image
features and the information asymmetry problem can be alleviated. With
additional classification and integrated triplet losses, our model can learn
expressive and discriminative image and video features for image-to-video
re-identification. Extensive experiments demonstrate the effectiveness of our
method and the overall results on two widely used datasets surpass the
state-of-the-art methods by a large margin. Code is available at:
this https URL

提出了一种新颖的 “时间知识传播（TKP）” 方法，该方法通过将视频表示网络学到的时间知识传播到图像表示网络来解决人物重识别问题中图像和视频特征不匹配的问题。通过大量实验，证明了该方法的有效性，并且在两个广泛使用的数据集上的综合结果明显优于现有的最先进方法。