Learning part-based features with multiple branches has been proven as an effective way to deliver high performance person re-identification. Existing works mostly exploit extra constraints on different branches to ensure the diversity of extracted features, which may lead to the increased complexity in network architecture and the difficulty for training. In this letter, we propose a quite simple multi-branch structure consisting of a global branch as well as a part branch in an asymmetrical way. We empirically demonstrate that such simple architecture can provide surprisingly high performance without imposing any extra constraint. On top of this, we further prompt the performance with a lightweight implementation of attention module. Extensive experimental results prove that the proposed method, termed Asymmetrical Network (AsNet), outperforms state-of-the-art methods with obvious margin on standard benchmark datasets such as Market1501, DukeMTMC, CUHK03. We believe that AsNet can serve as a strong baseline for related research and the source code is publicly available at https://github.com/www0wwwjs1/asnet.git.

中文翻译：

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AsNet：在行人重识别中学习丰富特征的非对称网络

使用多个分支学习基于部分的特征已被证明是提供高性能人员重新识别的有效方法。现有的工作大多利用对不同分支的额外约束来确保提取特征的多样性，这可能导致网络架构的复杂性和训练难度的增加。在这封信中，我们提出了一个非常简单的多分支结构，由一个全局分支和一个部分分支以不对称的方式组成。我们凭经验证明，这种简单的架构可以在不施加任何额外约束的情况下提供惊人的高性能。最重要的是，我们通过注意力模块的轻量级实现进一步提高了性能。大量的实验结果证明，所提出的方法，称为非对称网络 (AsNet)，在标准基准数据集（如 Market1501、DukeMTMC、CUHK03）上以明显的优势优于最先进的方法。我们相信 AsNet 可以作为相关研究的强大基线，源代码可在 https://github.com/www0wwwjs1/asnet.git 上公开获取。