论文标题
视频人的时间互补学习重新识别
Temporal Complementary Learning for Video Person Re-Identification
论文作者
论文摘要
本文提出了一个时间互补学习网络，该网络提取连续视频帧的互补特征，以供视频人员重新识别。首先，我们介绍了时间显着性擦除（TSE）模块，包括显着性擦除操作和一系列有序的学习者。具体而言，对于视频的特定框架，显着性擦除操作通过擦除以前框架激活的零件来驱动特定的学习者开采新的和互补的零件。因此，可以针对连续帧发现各种视觉特征，并最终构成目标身份的组成部分。此外，暂时的显着性提升（TSB）模块旨在传播视频框架之间的显着信息，以增强显着特征。通过有效减轻TSE擦除操作造成的信息损失，这是对TSE的补充。广泛的实验表明，我们的方法对最先进的方法表现出色。源代码可在https://github.com/blue-blue272/videoreid-tclnet上找到。
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.
