论文标题
封闭者重新识别的平行增强和双重增强
Parallel Augmentation and Dual Enhancement for Occluded Person Re-identification
论文作者
论文摘要
在过去的几十年中，在封闭的环境中搜索同一个人的图像的任务是遮挡的人重新识别（重新识别）。最近的方法集中于通过数据/功能增强或使用额外模型来预测遮挡的数据来改善性能。但是，他们忽略了此任务中的不平衡问题，无法完全利用培训数据中的信息。为了减轻这两个问题，我们提出了一种简单而有效的方法，该方法具有并行的增强和双重增强（PADE），该方法在封闭和非封闭式数据上都稳健，并且不需要任何辅助线索。首先，我们设计了一种并行的增强机制（PAM）来生成更合适的遮挡数据，以减轻不平衡数据的负面影响。其次，我们提出了全球和本地双重增强策略（DES），以促进上下文信息和细节。对三个广泛使用的封闭数据集和两个非封闭数据集的实验结果验证了我们方法的有效性。该代码可在https://github.com/littleprince1121/pade_paralleal_augmentation_and_dual_enhancement_for_occluded_person_person_reid获得。
Occluded person re-identification (Re-ID), the task of searching for the same person's images in occluded environments, has attracted lots of attention in the past decades. Recent approaches concentrate on improving performance on occluded data by data/feature augmentation or using extra models to predict occlusions. However, they ignore the imbalance problem in this task and can not fully utilize the information from the training data. To alleviate these two issues, we propose a simple yet effective method with Parallel Augmentation and Dual Enhancement (PADE), which is robust on both occluded and non-occluded data and does not require any auxiliary clues. First, we design a parallel augmentation mechanism (PAM) to generate more suitable occluded data to mitigate the negative effects of unbalanced data. Second, we propose the global and local dual enhancement strategy (DES) to promote the context information and details. Experimental results on three widely used occluded datasets and two non-occluded datasets validate the effectiveness of our method. The code is available at https://github.com/littleprince1121/PADE_Parallel_Augmentation_and_Dual_Enhancement_for_Occluded_Person_ReID
