# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on iLIDS-VID Dataset
#### 5.1.3 Comparison with the SOTA Methods  
In this section, we benchmark the performance of our improved GAF-Net, which utilizes the PiT architecture as the backbone for the appearance module and incorporates gait features, with other SOTA methods in the domain of person Re-ID. Our study emphasizes the significant improvements achieved by both fusion-level (concatenation) and score-level (weighted averaging) approaches, as discussed in the previous section. The evaluation was performed on the iLIDS-VID dataset, where our GAF-Net achieved the best performance in terms of accuracy, as shown in Table 5. Several conclusions can be drawn from our study, which are discussed below. By using PiT as the backbone, our GAF-Net increased rank-1 accuracy by 1.13% and rank-5 accuracy by 0.34% over the baseline PiT model with weighted sum fusion. Additionally, with concatenation, it improved by 1.00% and 0.41% for rank-1 and rank-5, respectively. These increases are significant, given the already high accuracy of the baseline model. Furthermore, the DenseIL method [8], which utilizes a hybrid framework of CNN-based and attention-based architectures, and the DCCT approach [11], which uses a coupled \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{4}{c}{**Appearance**} & \multicolumn{4}{c}{**Appearance + Gait**} \\  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline OSNet [16] & 59.20 & 82.60 & 89.34 & 94.80 & 70.26 (+11.06) & 89.73 (+7.13) & 93.86 (+4.52) & 97.66 (+2.86) \\ MGH [7] & 85.60 & 97.10 & 99.00 & 99.30 & 89.66 (+4.06) & 98.40 (+1.30) & 99.06 (+0.06) & 99.40 (+0.10) \\ PiT [10] & 92.07 & 98.93 & 99.80 & 100 & 93.00 (+2.67) & 99.00 (+0.00) & 99.60 (\(-0.36\)) & 99.73 (\(-0.27\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Person Re-ID performances (rank-1/rank-5/rank-10/rank-20) on the iLIDS-VID dataset according to the model and the integration of gait information. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{4}{c}{**Results**} \\  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline PiT [10] & 92.07 & 98.93 & 99.80 & **00** \\ DenseIL [8] & 92.00 & 98.00 & - & - \\ DCCT [11] & 91.70 & 98.60 & - & - \\ PSTA [9] & 91.50 & 98.10 & - & - \\ AA-RGTCN [49] & 90.60 & - & - & - \\ STRF [50] & 89.30 & - & - & - \\ MGH [7] & 85.6 & 97.10 & - & 99.50 \\ \hline GAF-Net (ours—concatenation) & 93.07 & **99.34** & **99.94** & **100** \\ GAF-Net (ours—score level) & **93.20** & 99.27 & 99.86 & **100** \\ \hline \hline \end{tabular} _Note:_ values indicate the best performance for each metric.  
\end{table} 
Table 5: Comparison with state-of-the-art backbones on iLIDS-VID. CNN with transformers, both demonstrate lower performance compared to GAF-Net. Our method surpassed DCCT by 1.5% and showed a slight advantage over DenseIL of 0.20% in rank-1 accuracy, illustrating the effectiveness of our dual fusion strategy. In comparison to Pyramid Spatial-Temporal Aggregation (PSTA) [9], which combines frame-level features with hierarchical temporal elements for detailed video-level representation, our GAF-Net achieved higher scores, outperforming PSTA by 1.7% in rank-1 while maintaining similar rank-5 performance. Furthermore, AA-RGTCN [49], which employs an adaptive alignment that effectively addresses frame misalignment and robustly captures temporal features, showed a rank-1 accuracy 2.6% lower than that of GAF-Net. This highlights GAF-Net's superior ability to effectively synthesize appearance and motion cues. Compared to the STRF approach [50], which integrates spatiotemporal representations through the use of 3D convolutional neural networks, our GAF-Net provided a significant performance boost, increasing rank-1 accuracy by 3.9%. Furthermore, compared to the multi-granular hypergraph (MGH) method [7], our GAF-Net achieved a substantial increase in accuracy, increasing rank-1 accuracy by 7.6% and rank-5 accuracy by 2.17%.