无监督学习下提升跨摄像头车辆检索准确率
Revisiting Multi-Granularity Representation via Group Contrastive Learning for Unsupervised Vehicle Re-identification
- 多粒度卷积网络提取可迁移特征，增强判别性
- 利用分组对比学习生成目标域伪标签，实现高效域自适应
- 无需人工标注，在真实场景中表现更优，适合大规模部署
车辆重识别（Vehicle ReID）旨在跨不同监控摄像头视图检索车辆图像。现有方法多依赖人工标注数据集进行训练，但在真实大规模场景中，由于源数据集与目标域之间存在显著领域差异，模型性能会急剧下降。为此，本文提出一种无监督车辆ReID框架MGR-GCL，结合多粒度卷积神经网络表示（MGR）以学习具有判别性的可迁移特征，并引入分组对比学习模块（GCL）在无标签目标域中生成伪标签，促进域自适应。在源域上预训练MGR后，通过GCL为目标域生成伪标签，实现高效适配。大量实验表明，该方法优于现有最先进方法。
原文摘要 · Abstract (English)
Vehicle re-identification (Vehicle ReID) aims at retrieving vehicle images across disjoint surveillance camera views. The majority of vehicle ReID research is heavily reliant upon supervisory labels from specific human-collected datasets for training. When applied to the large-scale real-world scenario, these models will experience dreadful performance declines due to the notable domain discrepancy between the source dataset and the target. To address this challenge, in this paper, we propose an unsupervised vehicle ReID framework (MGR-GCL). It integrates a multi-granularity CNN representation for learning discriminative transferable features and a contrastive learning module responsible for efficient domain adaptation in the unlabeled target domain. Specifically, after training the proposed Multi-Granularity Representation (MGR) on the labeled source dataset, we propose a group contrastive learning module (GCL) to generate pseudo labels for the target dataset, facilitating the domain adaptation process. We conducted extensive experiments and the results demonstrated our superiority against existing state-of-the-art methods.
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