In this paper, we present an efficient spatial-temporal representation for video person re-identification (reID). Firstly, we propose a Bilateral Complementary Network (BiCnet) for spatial complementarity modeling. Specifically, BiCnet contains two branches. Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts. On each branch, BiCnet appends multiple parallel and diverse attention modules to discover divergent body parts for consecutive frames, so as to obtain an integral characteristic of target identity. Furthermore, a Temporal Kernel Selection (TKS) block is designed to capture short-term as well as long-term temporal relations by an adaptive mode. TKS can be inserted into BiCnet at any depth to construct BiCnetTKS for spatial-temporal modeling. Experimental results on multiple benchmarks show that BiCnet-TKS outperforms state-of-the-arts with about 50% less computations. The source code is available at https://github.com/ blue-blue272/BiCnet-TKS.

本文提出了一种高效的时空表示方法BiCnet-TKS，并通过在原始分辨率和下采样策略上处理连续帧以获取目标身份的全面特征;该模型包含多个并行的和多样化的注意力模块以发现连续帧的不同部位，在任何深度插入TKS模块以捕捉短期和长期的时间关系，并在多个基准测试中取得优于先前研究的结果。

BiCnet-TKS: 学习视频人物再识别的高效时空表示