Video-based person re-identification matches video clips of people across
non-overlapping cameras. Most existing methods tackle this problem by encoding
each video frame in its entirety and computing an aggregate representation
across all frames. In practice, people are often partially occluded, which can
corrupt the extracted features. Instead, we propose a new spatiotemporal
attention model that automatically discovers a diverse set of distinctive body
parts. This allows useful information to be extracted from all frames without
succumbing to occlusions and misalignments. The network learns multiple spatial
attention models and employs a diversity regularization term to ensure multiple
models do not discover the same body part. Features extracted from local image
regions are organized by spatial attention model and are combined using
temporal attention. As a result, the network learns latent representations of
the face, torso and other body parts using the best available image patches
from the entire video sequence. Extensive evaluations on three datasets show
that our framework outperforms the state-of-the-art approaches by large margins
on multiple metrics.

提出了一种新的时空注意力模型，可以自动发现多样化的特征，并使用空间和时间的注意力组合提取有用的信息，从整个视频序列中学习潜在的面部、躯干和其他身体部位的隐藏表示，通过三个数据集的评估发现算法优于现有方法。