Learning representative, robust and discriminative information from images is essential for effective person re-identification (Re-Id). In this paper, we propose a compound approach for end-to-end discriminative deep feature learning for person Re-Id based on both body and hand images. We carefully design the Local-Aware Global Attention Network (LAGA-Net), a multi-branch deep network architecture consisting of one branch for spatial attention, one branch for channel attention, one branch for global feature representations and another branch for local feature representations. The attention branches focus on the relevant features of the image while suppressing the irrelevant backgrounds. In order to overcome the weakness of the attention mechanisms, equivariant to pixel shuffling, we integrate relative positional encodings into the spatial attention module to capture the spatial positions of pixels. The global branch intends to preserve the global context or structural information. For the the local branch, which intends to capture the fine-grained information, we perform uniform partitioning to generate stripes on the conv-layer horizontally. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. A set of ablation study shows that each component contributes to the increased performance of the LAGA-Net. Extensive evaluations on four popular body-based person Re-Id benchmarks and two publicly available hand datasets demonstrate that our proposed method consistently outperforms existing state-of-the-art methods.

翻译：从图像中学习具有代表性、鲁棒性和判别性的信息对于有效的行人重识别（Re-Id）至关重要。本文提出了一种基于身体和手部图像的端到端判别性深度特征学习的复合方法。我们精心设计了局部感知全局注意力网络（LAGA-Net），这是一种多分支深度网络架构，包含一个空间注意力分支、一个通道注意力分支、一个全局特征表示分支和一个局部特征表示分支。注意力分支聚焦于图像的相关特征，同时抑制不相关的背景。为了克服注意力机制对像素洗牌等变的缺陷，我们将相对位置编码集成到空间注意力模块中，以捕获像素的空间位置。全局分支旨在保留全局上下文或结构信息。对于旨在捕获细粒度信息的局部分支，我们在卷积层上均匀划分以生成水平条纹。我们通过软划分提取部件，无需显式划分图像或依赖外部线索（如姿态估计）。一系列消融研究表明，每个组件都促进了LAGA-Net性能的提升。在四个流行的基于身体的行人重识别基准和两个公开的手部数据集上的广泛评估表明，我们提出的方法始终优于现有最先进的方法。