Temporal Knowledge Propagation for Image-to-Video Person Re-identification
This addresses the information asymmetry in surveillance scenarios for improved person matching, but it is incremental as it builds on existing re-identification methods.
The paper tackles the problem of image-to-video person re-identification, where queries are images and galleries are videos, by proposing a Temporal Knowledge Propagation method to transfer temporal knowledge from videos to images, achieving state-of-the-art results with significant performance gains on two datasets.
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: https://github.com/guxinqian/TKP