论文标题
对抗性成对的对相机性能不平衡的重新识别重新识别：新数据集和指标
Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics
论文作者
论文摘要
人重新识别（人REID）模型的现有评估指标着重于系统范围的性能。但是，我们的研究揭示了由于摄像机之间的数据分布和不同的摄像头性能而导致的弱点，这些摄像头和不同的相机特性将REID系统暴露于剥削中。在这项工作中，我们提出了长期以来的REID摄像机性能不平衡问题，并从38个摄像机中收集了现实世界中的隐私性数据集，以帮助研究不平衡问题。我们提出了新的指标来量化相机性能不平衡，并进一步提出了对抗性成对的反向关注（APRA）模块，以指导模型学习相机不变特征，并具有新颖的成对注意力反转机制。
Existing evaluation metrics for Person Re-Identification (Person ReID) models focus on system-wide performance. However, our studies reveal weaknesses due to the uneven data distributions among cameras and different camera properties that expose the ReID system to exploitation. In this work, we raise the long-ignored ReID problem of camera performance imbalance and collect a real-world privacy-aware dataset from 38 cameras to assist the study of the imbalance issue. We propose new metrics to quantify camera performance imbalance and further propose the Adversarial Pairwise Reverse Attention (APRA) Module to guide the model learning the camera invariant feature with a novel pairwise attention inversion mechanism.
