Abstract
We empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address this issue, but they are largely confined to training domains of the models. We measure the camera bias of ReID models on unseen domains and reveal that camera bias becomes more pronounced under data distribution shifts. As a debiasing method for unseen domain data, we revisit feature normalization on embedding vectors. While the normalization has been used as a straightforward solution, its underlying causes and broader applicability remain unexplored. We analyze why this simple method is effective at reducing bias and show that it can be applied to detailed bias factors such as low-level image properties and body angle. Furthermore, we validate its generalizability across various models and benchmarks, highlighting its potential as a simple yet effective test-time postprocessing method for ReID. In addition, we explore the inherent risk of camera bias in unsupervised learning of ReID models. The unsupervised models remain highly biased towards camera labels even for seen domain data, indicating substantial room for improvement. Based on observations of the negative impact of camera-biased pseudo labels on training, we suggest simple training strategies to mitigate the bias. By applying these strategies to existing unsupervised learning algorithms, we show that significant performance improvements can be achieved with minor modifications.
Abstract (translated)
我们通过实证研究了人员重新识别（ReID）模型中的相机偏差。先前提出了几种针对该问题的相机感知方法，但这些方法主要局限于模型训练的数据域中。我们在未见过的数据域上测量了ReID模型的相机偏差，并揭示出在数据分布变化的情况下，这种偏见会变得更加明显。 作为应对未见领域数据偏见的方法，我们重新审视了嵌入向量上的特征归一化技术。尽管这种方法已被广泛采用作为一种简单的解决方案，其背后的原理和更广泛的适用性尚未得到充分探讨。我们分析了为什么这一简单方法在减少偏差方面如此有效，并展示了它可以应用于诸如低级图像属性和身体角度等详细偏见因素上。 此外，我们在多种模型和基准测试中验证了该方法的泛化能力，突显出其作为一种简单而有效的测试后处理方法在ReID中的潜在价值。同时，我们还探讨了无监督学习环境中ReID模型固有的相机偏差风险。即使对于已见过的数据域，这些无监督模型仍然对相机标签表现出强烈的偏见，表明仍有很大的改进空间。 基于对相机偏置伪标签对训练负面影响的观察，我们提出了一些简单有效的训练策略来缓解这种偏见。通过将这些策略应用于现有的无监督学习算法中，我们展示了只需进行少量修改即可实现显著的性能提升。
URL
https://arxiv.org/abs/2502.10195
