Abstract
Person identification in unconstrained viewing environments presents significant challenges due to variations in distance, viewpoint, imaging conditions, and clothing. We introduce $\textbf{E}$va $\textbf{C}$lothes-Change from $\textbf{H}$idden $\textbf{O}$bjects - $\textbf{B}$ody $\textbf{ID}$entification (ECHO-BID), a class of long-term re-id models built on object-pretrained EVA-02 Large backbones. We compare ECHO-BID to 9 other models that vary systematically in backbone architecture, model size, scale of object classification pretraining, and transfer learning protocol. Models were evaluated on benchmark datasets across constrained, unconstrained, and occluded settings. ECHO-BID, with transfer learning on the most challenging clothes-change data, achieved state-of-the-art results on long-term re-id -- substantially outperforming other methods. ECHO-BID also surpassed other methods by a wide margin in occluded viewing scenarios. A combination of increased model size and Masked Image Modeling during pretraining underlie ECHO-BID's strong performance on long-term re-id. Notably, a smaller, but more challenging transfer learning dataset, generalized better across datasets than a larger, less challenging one. However, the larger dataset with an additional fine-tuning step proved best on the most difficult data. Selecting the correct pretrained backbone architecture and transfer learning protocols can drive substantial gains in long-term re-id performance.
Abstract (translated)
在不受限制的观看环境中进行人员识别面临重大挑战，因为距离、视角、成像条件和着装变化都会引起变异。我们介绍了ECHO-BID（Eva Clothes-Change from Hidden Objects - Body IDentification），这是一个基于对象预训练的EVA-02 Large骨干网络构建的长期重识别模型系列。我们在基准数据集上对ECHO-BID和其他9种在骨干架构、模型大小、物体分类预训练规模和迁移学习协议方面系统变化的模型进行了评估，这些数据集涵盖了受限环境、非受限环境以及被遮挡场景。 通过在最具挑战性的着装变化数据上的转移学习，ECHO-BID在长期重识别任务上取得了最先进的结果，大幅超越了其他方法。此外，在观察角度受到遮挡的情况下，ECHO-BID也显著超过了其他方法。增加模型规模和在预训练期间使用Masked Image Modeling是ECHO-BID在长期重识别中表现出色的关键因素。 值得注意的是，尽管较小的、更具挑战性的转移学习数据集比较大的、较不具挑战性的数据集更好地泛化到不同的数据集中，但是更大的数据集加上额外的微调步骤对最困难的数据点表现最佳。选择适当的预训练骨干架构和迁移学习协议可以显著提高长期重识别性能。 总结来说，ECHO-BID通过使用改进的模型规模、特定的预训练技术以及有效的转移学习策略，在解决非受限环境下的人员长期识别问题上取得了突破性的进展。
URL
https://arxiv.org/abs/2507.17640
