Abstract
Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) methods. Recent works show that when labeled data originates from several distinct sources (e.g., datasets and cameras), considering each source separately and applying multi-source domain adaptation (MSDA) typically yields higher accuracy and robustness compared to blending the sources and performing conventional UDA. However, state-of-the-art MSDA methods learn domain-specific backbone models or require access to source domain data during adaptation, resulting in significant growth in training parameters and computational cost. In this paper, a Source-free Adaptive Gated Experts (SAGE-reID) method is introduced for person reID. Our SAGE-reID is a cost-effective, source-free MSDA method that first trains individual source-specific low-rank adapters (LoRA) through source-free UDA. Next, a lightweight gating network is introduced and trained to dynamically assign optimal merging weights for fusion of LoRA experts, enabling effective cross-domain knowledge transfer. While the number of backbone parameters remains constant across source domains, LoRA experts scale linearly but remain negligible in size (<= 2% of the backbone), reducing both the memory consumption and risk of overfitting. Extensive experiments conducted on three challenging benchmarks: Market-1501, DukeMTMC-reID, and MSMT17 indicate that SAGE-reID outperforms state-of-the-art methods while being computationally efficient.
Abstract (translated)
将人员再识别（Re-ID）模型适应到新的目标环境中仍然是一个挑战性问题，通常通过无监督领域自适应（UDA）方法来解决。最近的研究表明，当标注数据来自多个不同的来源时（例如，不同数据集或摄像机），分别考虑每个源并应用多源领域自适应（MSDA）相比于混合这些源然后执行传统的UDA往往能获得更高的准确性和鲁棒性。然而，最先进的MSDA方法学习特定领域的骨干模型或是需要在调整过程中访问源域的数据，这会导致训练参数的显著增加和计算成本的上升。 本文介绍了一种名为“无源自适应专家门控”（SAGE-reID）的方法来解决人员再识别问题。我们的SAGE-reID是一种高效的、无需源数据的MSDA方法，首先通过无监督领域自适应的方式训练每个来源特异性的低秩适配器（LoRA）。接着引入了一个轻量级的门控网络，并对其进行训练以动态分配融合LoRA专家时的最佳合并权重，从而实现有效的跨域知识转移。虽然骨干参数的数量在不同源域中保持不变，但LoRA专家数量线性增长却仍然很小（不超过骨干模型大小的2%），这减少了内存消耗和过拟合的风险。 我们在三个具有挑战性的基准数据集上进行了广泛的实验：Market-1501、DukeMTMC-reID 和 MSMT17。结果表明，SAGE-reID在计算效率方面超越了现有的最先进方法，并且性能更优。
URL
https://arxiv.org/abs/2508.06831
