Abstract
Person re-identification (re-id), which aims to retrieve images of the same person in a given image from a database, is one of the most practical image recognition applications. In the real world, however, the environments that the images are taken from change over time. This causes a distribution shift between training and testing and degrades the performance of re-id. To maintain re-id performance, models should continue adapting to the test environment's temporal changes. Test-time adaptation (TTA), which aims to adapt models to the test environment with only unlabeled test data, is a promising way to handle this problem because TTA can adapt models instantly in the test environment. However, the previous TTA methods are designed for classification and cannot be directly applied to re-id. This is because the set of people's identities in the dataset differs between training and testing in re-id, whereas the set of classes is fixed in the current TTA methods designed for classification. To improve re-id performance in changing test environments, we propose TEst-time similarity Modification for Person re-identification (TEMP), a novel TTA method for re-id. TEMP is the first fully TTA method for re-id, which does not require any modification to pre-training. Inspired by TTA methods that refine the prediction uncertainty in classification, we aim to refine the uncertainty in re-id. However, the uncertainty cannot be computed in the same way as classification in re-id since it is an open-set task, which does not share person labels between training and testing. Hence, we propose re-id entropy, an alternative uncertainty measure for re-id computed based on the similarity between the feature vectors. Experiments show that the re-id entropy can measure the uncertainty on re-id and TEMP improves the performance of re-id in online settings where the distribution changes over time.
Abstract (translated)
人物识别（RE-ID）是一种从数据库中检索相同人物图像的图像识别应用，是实践中最实用的图像识别应用之一。然而，在现实生活中，照片拍摄的环境会随着时间的推移而变化，这会导致训练和测试之间的分布转移，从而降低RE-ID的性能。为了保持RE-ID的性能，模型应继续适应测试环境的时变性。测试时间适应（TTA）是一种通过仅使用未标记测试数据来适应测试环境的方法，是解决这个问题的一个有前途的方法，因为TTA可以在测试环境中立即适应模型。然而，之前的设计为分类的TTA方法无法直接应用于RE-ID。这是因为数据集中的人名集合在训练和测试环境之间有所不同，而当前为分类设计的TTA方法中的集合是固定的。为了在变化的环境中提高RE-ID的性能，我们提出了TEst-time similarity Modification for Person re-identification（TEMP），一种新颖的RE-ID TTA方法。TEMP是第一个完全的RE-ID TTA方法，不需要对预训练进行修改。受到分类TTA方法精炼预测不确定性的启发，我们试图提高RE-ID的不确定性。然而，由于RE-ID是一个开放集任务，它不共享在训练和测试环境之间的人名标签，因此我们提出了RE-ID entropy，一种基于特征向量之间相似性计算的RE-ID alternative uncertainty measure。实验证明，RE-ID熵可以衡量RE-ID的不确定性，而TEMP的性能在在线设置中提高了RE-ID的性能，这些设置中分布会随时间变化。
URL
https://arxiv.org/abs/2403.14114
