Abstract
This study introduces a novel framework, "Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-identification (CORE-ReID)", to address an Unsupervised Domain Adaptation (UDA) for Person Re-identification (ReID). The framework utilizes CycleGAN to generate diverse data that harmonizes differences in image characteristics from different camera sources in the pre-training stage. In the fine-tuning stage, based on a pair of teacher-student networks, the framework integrates multi-view features for multi-level clustering to derive diverse pseudo labels. A learnable Ensemble Fusion component that focuses on fine-grained local information within global features is introduced to enhance learning comprehensiveness and avoid ambiguity associated with multiple pseudo-labels. Experimental results on three common UDAs in Person ReID demonstrate significant performance gains over state-of-the-art approaches. Additional enhancements, such as Efficient Channel Attention Block and Bidirectional Mean Feature Normalization mitigate deviation effects and adaptive fusion of global and local features using the ResNet-based model, further strengthening the framework. The proposed framework ensures clarity in fusion features, avoids ambiguity, and achieves high ac-curacy in terms of Mean Average Precision, Top-1, Top-5, and Top-10, positioning it as an advanced and effective solution for the UDA in Person ReID. Our codes and models are available at this https URL.
Abstract (translated)
这项研究提出了一种新颖的框架，即“通过领域适应进行集合融合以实现人员再识别（ReID）的全面优化和改进（CORE-ReID）”，旨在解决无监督领域适应（UDA）在人员重识别中的问题。该框架利用CycleGAN生成多样化的数据，在预训练阶段平衡来自不同摄像机源图像特征之间的差异。在微调阶段，基于一对教师-学生网络，该框架整合多视角特征进行多层次聚类以推导出多种伪标签。为了增强学习的全面性并避免与多个伪标签相关的模糊性，引入了一个可学习的集合融合组件，专注于从全局特征中提取细粒度局部信息。在三项常见的UDA实验中，Person ReID展示了显著优于现有方法的表现。此外，通过基于ResNet模型的有效通道注意力块和双向均值特征归一化进一步增强了框架的功能，以减少偏差效应，并适应性地融合全局与局部特征。所提出的框架确保了融合特征的清晰度，避免了模糊性，并在平均精度、Top-1、Top-5和Top-10指标上实现了高准确性，使其成为人员ReID领域UDA问题的一个先进且有效的解决方案。我们的代码和模型可在以下链接获取：[提供的URL]。
URL
https://arxiv.org/abs/2508.03064
