论文标题
看起来迷人：在全球和本地关注的异质摄像机网络中的车辆重新ID
Looking GLAMORous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention
论文作者
论文摘要
车辆重新识别（RE-ID）是现代监视摄像机网络的基本问题。车辆重新ID的现有方法通过组合多个子网和损失，利用全球功能和本地功能来重新ID。在本文中，我们提出了Re-ID的魅力或全球和本地关注模块。魅力在统一模型中同时执行全球和本地特征提取，以在各种对抗条件和数据集（MAPS 80.34，76.48，77.15上分别在Veri-776上，分别为VIR-776，VRIC和VERI-WILD）实现车辆重新ID的最新性能（MAPS 80.34，76.48，77.15）。魅力引入了几种贡献：一种更好的主干结构方法，它胜过最近的方法，小组和层归一化，以解决重新ID的冲突损失目标，一个新型的全球注意力集体模块，用于全球特征提取的新型全球注意力模块，以及一个新的基于零件的局部特征提取的本地注意模块，不需要监督。此外，魅力是一种紧凑而快速的模型，较小10倍，同时提供25％的性能。
Vehicle re-identification (re-id) is a fundamental problem for modern surveillance camera networks. Existing approaches for vehicle re-id utilize global features and local features for re-id by combining multiple subnetworks and losses. In this paper, we propose GLAMOR, or Global and Local Attention MOdules for Re-id. GLAMOR performs global and local feature extraction simultaneously in a unified model to achieve state-of-the-art performance in vehicle re-id across a variety of adversarial conditions and datasets (mAPs 80.34, 76.48, 77.15 on VeRi-776, VRIC, and VeRi-Wild, respectively). GLAMOR introduces several contributions: a better backbone construction method that outperforms recent approaches, group and layer normalization to address conflicting loss targets for re-id, a novel global attention module for global feature extraction, and a novel local attention module for self-guided part-based local feature extraction that does not require supervision. Additionally, GLAMOR is a compact and fast model that is 10x smaller while delivering 25% better performance.
