# Establishing Two-Dimensional Dependencies for Multi-Label Image Classification
## 4 Experiments
### Experimental Results
#### 4.4.3 Ablation Studies  
As shown in this section, we conducted ablation studies from four different aspects, including the impact of different modules in TDDM on classification results; the influence of calculation methods for fusion factor \(B_{f}\) and compensation factor \(C_{f}\) in FFM feature fusion; the effects of weight proportion control coefficients \(\alpha\) and \(\beta\) in FEM feature enhancement; and the influence of threshold values \(\tau\) and \(p\) in GCN for computing correlation matrices. We conducted the aforementioned ablation experiments on the VOC dataset. **Effects of different modules. ** To evaluate the effectiveness of our model, we conducted ablation studies by reconstructing the model with different factors. To validate the necessity of modeling dependencies in both the image and semantic dimensions, we conducted ablation experiments by individually removing the SFDM and LSDM branches, as shown in the second and third rows of Table 5. The results indicate that using either the SFDM or LSDM branch alone yielded inferior performance compared to modeling dependencies in both dimensions. This strongly demonstrates the complementarity of the two branches and the effectiveness of modeling image feature spatial dependencies and semantic label dependencies. Furthermore, to further verify the effectiveness of each module in the dual branches, we conducted individual ablation experiments on each module. As the GCN module is concatenated before the GREM and serves as the foundation for LSDM, it cannot be individually ablated. Therefore, we selected FFM, FEM, and GREM as separate ablation modules. The results of the ablation experiments are presented in rows four to six of Table 5. The experimental results thoroughly validated the effectiveness of each module. We conducted statistical significance tests for the individual modules in the ablation study using Wilcoxon signed-rank tests. The results, as shown in Table 6, indicate that the \(p\)-values for all comparisons were below 0.05, confirming that the observed differences were statistically significant. Additionally, we further analyzed the impact of each module by examining the mAP values, and the results demonstrate that each module contributed significantly to the model's performance. **Effects of different \(B_{f}\) and \(C_{f}\). ** Determining the calculation of the fusion factor \(B_{f}\) and the compensation factor \(C_{f}\) in FFM is a key aspect that warrants our attention. Additionally, we aimed to ensure that the computation method does not introduce any additional computational burden. Therefore, we explored relatively straightforward approaches, such as the element-wise addition (add) or multiplication (mul) of feature vectors, to perform these calculations. The combined methods and identification results are presented in Table 7. We explored four fusion methods in various combinations, and it can be observed that adding the feature vectors as the fusion factor \(B_{f}\) and multiplying the feature vectors as the compensation factor \(C_{f}\) yielded the best combination approach.