# Contextual Coefficients Excitation Feature: Focal Visual Representation for Relationship Detection
## 5 Comparison and Discussion
In order to answer this question, the evolution of the relationship classification loss which is a function of epochs is shown in Figure 5. In general, the classification loss \(l_{rel}\) decreases steadily over training, showing the success of our model in mapping. Another relevant question is whether our method keep convergent or not for generating coefficients, compared to the other methods. Figure 5 shows the Recall@50 of relationship detection as the function of epochs with different generation methods, e.g., Boundary Equilibrium Generative Adversarial Networks(BEGAN) [41], Least Squares Generative Adversarial Networks(LSGAN) [42] and original GAN [20], which are trained with the same setting and structure. In particular, LSGAN and BEGAN have more prone to training crashes and produce more serious training instability results even compared with the original GAN. While the stable training trend and loss convergence during the training of WGAN is observed, similar conclusions are also drawn from the recent work [43]. After verifying that our method leads to more stable training performance than the other methods, the generalization ability of different methods is another critical problem. Hence, the results of predicate detection on entire set and zero-shot with different generative models are compared in Table 3. In both the entire set and zero-shot setting, our model has the best performance for generating highly suitable coefficients. And the GAN models have the better performance of generalization on both entire set and zero-shot and converge faster than cVAE. We conjecture that VAE tends to produce blurred results and leads to the inaccurate coefficients. In addition, the generalization ability of our model is also affected by the balance term \(\alpha\) in (9), as shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Entire Set**} & \multicolumn{4}{c}{**Zero-Shot**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Phrase Det**} & \multicolumn{2}{c}{**Relationship Det**} & \multicolumn{2}{c}{**Phrase Det**} & \multicolumn{2}{c}{**Relationship Det**} \\  & **R@100** & **R@50** & **R@100** & **R@50** & **R@100** & **R@50** & **R@100** & **R@50** \\ \hline Language Priors [8] & 17.0 & 16.2 & 14.7 & 13.9 & 3.8 & 3.4 & 3.5 & 3.1 \\ Visual Spatial [15] & 18.9 & 16.9 & 15.8 & 14.3 & 3.5 & 3.2 & 3.0 & 2.9 \\ TFR [39] & 19.1 & 17.4 & 16.8 & 15.2 & 7.1 & 5.8 & 6.5 & 5.3 \\ CAI [7] & 19.2 & 17.6 & 17.4 & 15.6 & 6.6 & 6.0 & 6.0 & 5.5 \\ SA-Full [40] & 19.5 & **17.9** & 17.1 & **15.8** & 7.8 & 6.8 & 7.4 & 6.4 \\ \hline Appearance & 16.9 & 15.4 & 14.8 & 13.5 & 5.9 & 5.3 & 5.5 & 5.0 \\ Ours \(-\) A + S & 18.2 & 16.3 & 15.9 & 14.2 & 8.4 & 7.1 & 7.6 & 6.5 \\ Ours \(-\) A + L & 19.4 & 17.8 & 16.9 & 15.6 & 6.2 & 5.9 & 5.6 & 5.2 \\ Ours \(-\) A + S + L & 19.8 & 17.3 & 17.2 & 15.1 & 8.0 & 6.3 & 7.0 & 5.7 \\ CCEF (Ours \(-\) A + S + G) & **20.3** & 17.4 & **17.6** & 15.2 & **10.0** & **7.5** & **8.4** & **7.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of phrase detection and relationship detection on VRD and Zero-Shot Set (ZS). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Entire Set.** & **Zero-Shot.** \\  & **R@100/50** & **R@100/50** \\ \hline cVAE [44] & 37.5 & 14.8 \\ cGAN [20] & 54.5 & 20.8 \\ LSGAN [42] & 52.9 & 20.7 \\ BEGAN [41] & 54.2 & 21.6 \\ CCEF & **55.5** & **23.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different Generation Methods in Predicate Detection. **Comparison with Attention Methods. ** To tackle the problem of relationship-independent visual appearance, another promising approach is attention mechanism, which encourages the network to focus on the discriminative regions of feature map during extracting the visual features. Therefore, the detection results of existing attention methods with our excitation approach and choose ResNet-50 [45] as the feature extractor in Figure 2 are compared to determine the effect of visual feature distributions.