  Image-to-video person re-identification identifies a target person by a probe
image from quantities of pedestrian videos captured by non-overlapping cameras.
Despite the great progress achieved,it's still challenging to match in the
multimodal scenario,i.e. between image and video. Currently,state-of-the-art
approaches mainly focus on the task-specific data,neglecting the extra
information on the different but related tasks. In this paper,we propose an
end-to-end neural network framework for image-to-video person reidentification
by leveraging cross-modal embeddings learned from extra information.Concretely
speaking,cross-modal embeddings from image captioning and video captioning
models are reused to help learned features be projected into a coordinated
space,where similarity can be directly computed. Besides,training steps from
fixed model reuse approach are integrated into our framework,which can
incorporate beneficial information and eventually make the target networks
independent of existing models. Apart from that,our proposed framework resorts
to CNNs and LSTMs for extracting visual and spatiotemporal features,and
combines the strengths of identification and verification model to improve the
discriminative ability of the learned feature. The experimental results
demonstrate the effectiveness of our framework on narrowing down the gap
between heterogeneous data and obtaining observable improvement in
image-to-video person re-identification.
