# Cross-Domain Person Re-Identification Based on Feature Fusion Invariance
## 3 Experimental Results
### Ablation Experiment
#### 3.1.1 Invariance Analysis Based on Feature Fusion  
Validation experiments were carried out on the Market-1501 [29] and DukeMTMC-reID [30] datasets to investigate the effectiveness of the proposed feature fusion and cross-domain feature invariance learning methods for the target domain. The experiment was divided into two groups. In one group, DukeMTMC-reID\(\rightarrow\)Market-150 refers to DukeMTMC-reID as the source domain and Market-1501 as the target domain. The other set Market-1501\(\rightarrow\)DukeMTMC-reID has the opposite effect. In this paper, global features are taken as the baseline of the experiment and recorded as GAP, and local features based on an attention mechanism are recorded as PAF (part-aligned feature). The features based on the fusion of the attention mechanism are denoted as attention fusion features (AFFs), and cross-domain feature invariance learning is denoted as Inv. GAP, FAP, AFF, Inv + GAP, Inv + FAP, Inv + GAP+FAP, and a combination of Inv + AFF are used to verify the validity of each part. (1) Effectiveness analysis of local features  
As shown in Table 1, in the cross-domain experiment from the DukeMTMC-reID dataset to the Market-1501 dataset, the average accuracy mAP based on the local feature (PAF) increased by 17.6%, and the rank 1 increased by 10% compared with the global feature baseline (GAP). In the comparison of Inv + GAP + PAF and Inv + GAP, the two evaluation indices increasedby 12.9% and 6.2%, respectively. In the cross-domain experiment from the Market-1501 dataset to the DukeMTMC-reID dataset, PAF increased by 16% and 11.4% compared with GAP, and Inv + GAP + PAF increased by 11.3% and 9.1% compared with Inv + GAP. The experimental results show that local features can focus on more fine-grained pedestrian features, and it is necessary to use local features to train and optimize the network model. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Market-1501**} & \multicolumn{8}{c}{**DukeMTMC-reID**} \\ \cline{2-11}  & **Src** & **R-1** & **R-5** & **R-10** & **R-20** & **mAP** & **Src** & **R-1** & **R-5** & **R-10** & **R-20** & **mAP** \\ \hline GAP & & 65.7 & 81.5 & 86.0 & 89.9 & 31.9 & & 52.7 & 64.7 & 69.7 & 73.4 & 27.0 \\ PAF & & 75.7 & 89.4 & 92.0 & 94.3 & 49.5 & & 64.1 & 76.5 & 80.3 & 83.8 & 43.0 \\ AFF & & 77.6 & 89.7 & 92.0 & 95.2 & 57.0 & & 66.5 & 78.6 & 82.1 & 86.5 & 50.8 \\ Inv + GAP & & 81.2 & 92.1 & 93.7 & 96.6 & 65.4 & & 71.6 & 81.0 & 84.2 & 88.3 & 59.0 \\ Inv + PAF & & 86.4 & 93.1 & 95.5 & 97.8 & 76.6 & & 78.5 & 84.7 & 90.3 & 91.7 & 68.4 \\ Inv + GAP + PAF & & 87.4 & 94.9 & 96.0 & 98.5 & 78.1 & & 80.1 & 86.0 & 92.1 & 94.6 & 70.3 \\
**Inv + AFF** & & **90.6** & **96.4** & **98.3** & **99.3** & **82.2** & & **82.5** & **87.7** & **93.6** & **95.4** & **71.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance ablation experiments comparison of methods when tested on Market-1501 and DukeMTMC-reID. Rank-n metrics assess the proportion of correct matches within the top n results when querying pedestrian images, where rank-1 demands the correct match at the first position. Rank-5, rank-10, and rank-20 progressively relax this criterion, examining the top 5, 10, and 20 results, respectively. mAP (mean Average Precision) evaluates the average precision, reflecting the overall performance of a retrieval system by combining the true positive rate and the ranking of results across all queries. Figure 4: Comparison of the effectiveness of loss functions in ablation experiments. Effectiveness of invariant learning  
In two cross-domain experiments, Inv + GAP increased by 33.5% and 15.5% and 32% and 18.9% compared with GAP, respectively. Compared with those of PAF, those of Inv + PAF increased by 27.1%, 10.7%, 25.4%, and 14.4%, respectively. For both local features and local and global fusion features, the addition of cross-domain feature invariance learning improves the pedestrian rerecognition performance.