Abstract
The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method has robustly achieved state-of-the-art performances and outperformed any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we achieve a state-of-the-art result of Rank-1/mAP=96.6%/94.2% after re-ranking.
Abstract (translated)
全局和部分特征的组合已经成为改善人员重新识别（Re-ID）任务中的判别性能的基本解决方案。以前基于部分的方法主要侧重于定位具有特定预定义语义的区域以学习局部表示，这增加了学习难度，但对于具有大差异的场景不具有效率或鲁棒性。在本文中，我们提出了一种将判别信息与各种粒度相结合的端到端特征学习策略。我们精心设计了多粒度网络（MGN），这是一种多分支深度网络架构，包括一个用于全局特征表示的分支和两个用于局部特征表示的分支。我们不是学习语义区域，而是将图像统一划分为多个条带，并改变不同本地分支中的部分数量，以获得具有多个粒度的局部特征表示。在包括Market-1501，DukeMTMC-reid和CUHK03在内的主流评估数据集上实施的综合实验表明，我们的方法已经稳健地实现了最先进的性能，并且大大优于任何现有方法。例如，在单一查询模式下的Market-1501数据集中，我们在重新排名后获得Rank-1 / mAP = 96.6％/ 94.2％的最新结果。
URL
https://arxiv.org/abs/1804.01438
