  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.
