Recent years have witnessed the remarkable progress of applying deep learning
models in video person re-identification (Re-ID). A key factor for video person
Re-ID is to effectively construct discriminative and robust video feature
representations for many complicated situations. Part-based approaches employ
spatial and temporal attention to extract representative local features. While
correlations between parts are ignored in the previous methods, to leverage the
relations of different parts, we propose an innovative adaptive graph
representation learning scheme for video person Re-ID, which enables the
contextual interactions between relevant regional features. Specifically, we
exploit the pose alignment connection and the feature affinity connection to
construct an adaptive structure-aware adjacency graph, which models the
intrinsic relations between graph nodes. We perform feature propagation on the
adjacency graph to refine regional features iteratively, and the neighbor
nodes' information is taken into account for part feature representation. To
learn compact and discriminative representations, we further propose a novel
temporal resolution-aware regularization, which enforces the consistency among
different temporal resolutions for the same identities. We conduct extensive
evaluations on four benchmarks, i.e. iLIDS-VID, PRID2011, MARS, and
DukeMTMC-VideoReID, experimental results achieve the competitive performance
which demonstrates the effectiveness of our proposed method. The code is
available at this https URL

提出一种创新性的自适应图形表示学习方案，用于视频人员重识别；该方案利用姿势对齐连接和特征亲和连接构建自适应结构感知邻接图，以迭代地完善区域特征，并将周围节点信息考虑在部分特征表示中；进一步提出了一种新的时间分辨率感知正则化，用于学习紧凑而区分性的特征表示。