# Cross-Domain Person Re-Identification Based on Feature Fusion Invariance
## 4 Conclusions  
In response to the challenges posed by scarce annotation resources and pronounced domain discrepancies, we devise a cross-domain person re-identification model. Central to this model is a strategy rooted in the invariance of feature fusion, aimed at bridging the data domain gap and achieving efficient, robust person matching.  
Our model dispenses with the need for intricate part annotations in the source domain, relying solely on person identity labels. It ingeniously integrates attention-guided extraction and fusion mechanisms for both global and local features. By constructing a local feature graph and employing Graph Attention Networks (GAT), it adeptly discriminates and aggregates neighboring node information. Leveraging multi-scale channel attention modules, it then deeply integrates these global and local features, capturing not only the overall structure of the person but also meticulously detailing their local characteristics, thereby realizing profound comprehension and precise depiction of person features.  
To tackle the unlabeled data challenge in the target domain, we adopt an unsupervised learning strategy to effectively excavate and distill the essence of person features. Building upon this, we incorporate a feature memory module, which effectively stores and organizes the fused pedestrian features learned from the target domain, with a particular emphasis on ensuring that these features retain the distinctive distribution characteristics inherent to the target domain.  
In pursuit of consistent performance across domains, we meticulously craft a cross-domain invariance loss function designed to minimize disparities between feature spaces of the source and target domains, significantly enhancing the model's generalization capabilities and robustness under diverse environmental conditions and varying camera configurations.  
Extensive experimental validations, conducted using the two cross-domain datasets--DukeMTMC-reID to Market-1501 and vice versa--conclusively demonstrate that our proposed method achieves substantial performance leaps in both cross-domain tasks. The marked improvements in key metrics such as identification accuracy and mean Average Precision (mAP) resoundingly validate the exceptional efficacy and practical value of our approach in addressing the complexities of cross-domain pedestrian re-identification.  
Figure 6: Visualization of the refined parts under different \(p\) values. (**a**) shows the visualization for \(p\) set to 6, (**b**) shows the visualization when \(p\) is set to 8, and (**c**) shows the visualization with \(p\) set to 12. When \(p=8\) or 12, some parts repeat with others or become empty.  
**Author Contributions:** Conceptualization, Y.Z.; methodology, H.S.; software, J.W.; validation, Y.Z., H.S. and J.W.; formal analysis, Y.Z. and H.S.; investigation, Y.Z. and H.S.; resources, J.W.; data curation, Y.Z. and H.S.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., H.S. and J.W.; visualization, Y.Z.; supervision, J.W.; project administration, H.S. and J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 62172193).  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.