Video-based person re-identification (Re-ID) aims to automatically retrieve
video sequences of the same person under non-overlapping cameras. To achieve
this goal, it is the key to fully utilize abundant spatial and temporal cues in
videos. Existing methods usually focus on the most conspicuous image regions,
thus they may easily miss out fine-grained clues due to the person varieties in
image sequences. To address above issues, in this paper, we propose a novel
Global-guided Reciprocal Learning (GRL) framework for video-based person Re-ID.
Specifically, we first propose a Global-guided Correlation Estimation (GCE) to
generate feature correlation maps of local features and global features, which
help to localize the high- and low-correlation regions for identifying the same
person. After that, the discriminative features are disentangled into
high-correlation features and low-correlation features under the guidance of
the global representations. Moreover, a novel Temporal Reciprocal Learning
(TRL) mechanism is designed to sequentially enhance the high-correlation
semantic information and accumulate the low-correlation sub-critical clues.
Extensive experiments are conducted on three public benchmarks. The
experimental results indicate that our approach can achieve better performance
than other state-of-the-art approaches. The code is released at
this https URL

该论文提出了一种新颖的全球引导的互逆学习（GRL）框架，其中包括全局引导相关估计（GCE）和时态互逆学习（TRL）机制，用于视频人员重新识别，可以更好地利用视频中的空间和时间线索，并在三个公共基准上实现了比其他现有技术更好的表现。