# Contextual Coefficients Excitation Feature: Focal Visual Representation for Relationship Detection
## 5 Comparison and Discussion  
**Predicate Detection. ** The results of predicate detection are reported in Table 1. The benefit of visual feature excitation is visible across all experiment settings: all kind of coefficients are effective while CCEF achieves better performance. (e.g., R@50 is improved from 45.2% to 55.5% on the entire set and from 13.2% to 23.2% on the zero-shot set). In Table 1, (Appearance) only uses visual information and gets unsatisfactory performance. Then the spatial information is added in (Ours \(-\) A + S), which can enhance local visual feature where objects are more likely to interact, and improves the performance of 3 points. Next, the label semantic information is introduced in (Ours \(-\) A + L), instead of the spatial information. It reweights the identity information corresponding to the object category in the visual features, which significantly improves the performance of 5 points. Now we can see the performance of (Ours \(-\) A + L + S) is 53.0 (\(\approx 45.2+3+5\)), which proves that label and spatial information can effectively complementary. In the end, CCEF (Ours \(-\) A + S + G) replaces the label coefficient (L) in (Ours \(-\) A + L + S) with the generated-label coefficient (G) synthesized by the conditional WGAN. We can see the total performance of CCEF (Ours \(-\) A + S + G) improves by from 53.0 to 55.5, as shown in Figure 4, which proves that the generated-label coefficient G can effectively improve model generalization and the prediction ability of the unseen relationship, thus improves the overall performance. The G effectively improve model generalization and the prediction ability of the unseen relationship, as shown in Figure 4. Hence, the total performance of CCEF (Ours \(-\) A + S + G) improves by from 52.3 to 55.5. Another interesting finding is that the (Ours \(-\) A + S) shows significant improvement on zero-shot. We speculate the reason is that spatial coefficients come from the spatial features which are triplet-independent and are less susceptible to the long tail distribution of training data. **Phrase and Relationship Detection. ** To fairly compare the performance of relationship models, the same object detection results [8; 15] are utilized. Relationship for every pair of object is predicted with the pipeline in Figure 2. Evaluation results on the entire test set and the zero-shot setting are shown in Table 2. The observations on various experiments are consistent with predicate detection. However, the spatial coefficients have less improvement in phrase or relationship detection than predicate detection due to the position error of object detection. Even so, performance of CCEF (Ours \(-\) A + S + G) is still better than other previous models, especially under the zero-shot setting. **Effect of Generation Methods. ** An important question about our approach is whether the generative methods succeed in mapping label embedding to suitable coefficients for relationship. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Entire Set** & **Zero-Shot** \\  & **R@100/50**1 & **R@100/50** \\ \hline Language Priors [8] & 47.9 & 8.5 \\ VTransE [16] & 44.8 & \(-\) \\ STA [37] & 48.0 & 20.6 \\ VSA-Net [38] & 49.2 & \(-\) \\ Zoom-Net [17] & 50.7 & \(-\) \\ LK [9] * & 47.5 & 17.0 \\ Visual Spatial [15] & 51.5 & 14.6 \\ TFR [39] & 52.3 & 17.3 \\ SA-Full [40] & 52.6 & 21.6 \\ CAI [7] & 53.6 & 16.4 \\ \hline Appearance & 45.2 & 13.2 \\ Ours \(-\) A + S & 48.5 & 20.4 \\ Ours \(-\) A + L & 50.5 & 15.4 \\ Ours \(-\) A + S + L & 53.0 & 19.5 \\ CCEF (Ours \(-\) A + S + G) & **55.5** & **23.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of different methods on VRD including the R@100/50 of predicate detection on entire and zero-shot set. “** marks the results of LK without knowledge distillation. And “\(-\)” indicates “not applicable”. Figure 4: Performance of different sizes of training examples. “CCEF” and “Appearance” have the same setting as Section 4.