  Recently, the research interest of person re-identification (ReID) has
gradually turned to video-based methods, which acquire a person representation
by aggregating frame features of an entire video. However, existing video-based
ReID methods do not consider the semantic difference brought by the outputs of
different network stages, which potentially compromises the information
richness of the person features. Furthermore, traditional methods ignore
important relationship among frames, which causes information redundancy in
fusion along the time axis. To address these issues, we propose a novel general
temporal fusion framework to aggregate frame features on both semantic aspect
and time aspect. As for the semantic aspect, a multi-stage fusion network is
explored to fuse richer frame features at multiple semantic levels, which can
effectively reduce the information loss caused by the traditional single-stage
fusion. While, for the time axis, the existing intra-frame attention method is
improved by adding a novel inter-frame attention module, which effectively
reduces the information redundancy in temporal fusion by taking the
relationship among frames into consideration. The experimental results show
that our approach can effectively improve the video-based re-identification
accuracy, achieving the state-of-the-art performance.
