This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification. Firstly, we introduce a Temporal Saliency Erasing (TSE) module including a saliency erasing operation and a series of ordered learners. Specifically, for a specific frame of a video, the saliency erasing operation drives the specific learner to mine new and complementary parts by erasing the parts activated by previous frames. Such that the diverse visual features can be discovered for consecutive frames and finally form an integral characteristic of the target identity. Furthermore, a Temporal Saliency Boosting (TSB) module is designed to propagate the salient information among video frames to enhance the salient feature. It is complementary to TSE by effectively alleviating the information loss caused by the erasing operation of TSE. Extensive experiments show our method performs favorably against state-of-the-arts. The source code is available at https://github.com/blue-blue272/VideoReID-TCLNet.

本文提出了一种时序补充学习网络，旨在提取连续视频帧的补充特征，用于视频中的人员再识别，并通过时序显著性抹除和增强模块，有效地实现信息增强和丢失的平衡，实验结果表明其优于现有技术。

视频人物再识别的时空互补学习