# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 4 Experiments
### Detector Comparison  
In the previous experiment, surrounding information is obtained by detecting and recognizing surrounding objects using YOLOv2 on 80 categories. However, of those 80 categories, we use the results from only 65 that are disjoint from VG's unseen categories. That limited reference to surrounding objects for a somewhat small amount of classification categories could lead to poor performance improvement. For ascertainment, we conduct an additional experiment by applying another pretrained detector with a large scale category, YOLOv2-9K [54], and applying the other source, ground-truths for seen class labels. YOLOv2-9K produces detected results above the confidence threshold of 0.1 for 8955 categories out of 9K that have available semantic embedding and disjoint from unseen categories. In both YOLOv2-80 and YOLOv2-9K, we exclude detections whose intersection-over-union with the bounding box of the target object is 0.8 or higher. Ground-truths for seen class labels, GT\({}_{\text{S}}\), are applied as surrounding information with a fixed confidence by exploiting bounding boxes from annotations on the seen class labels in the test images. The value of the prediction probability \(p\) in Equation (2) is fixed to 1.0 in GT\({}_{\text{S}}\). To alleviate the problem of noise caused by high-frequency objects, such as a category, window in the ground-truths, we set the system to reference only one randomly selected object per class label. The evaluation result of the variation in detection of surrounding objects is presented in Table 6. The performance of YOLOv2-9K is generally poor because of its tendency to return detection results for \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Classic**} & \multicolumn{4}{c}{**Generalized**} \\ \cline{2-10}  & & \multicolumn{2}{c}{**hit@1**} & \multicolumn{2}{c}{**hit@5**} & \multicolumn{2}{c}{**hit@1**} & \multicolumn{2}{c}{**hit@5**} \\  & & **per-cls** & **per-ins** & **per-cls** & **per-ins** & **per-cls** & **per-ins** & **per-cls** & **per-ins** \\ \hline ConSE (**baseline**) & & 14.99 & 31.62 & 34.45 & 57.64 & 0.05 & 0.29 & 13.97 & 32.28 \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & path & 15.29 & 32 & 34.64 & 57.86 & 0.2 & 0.29 & 13.97 & 32.31 \\  & lch & 15.17 & 32.02 & 34.55 & 57.76 & 0.23 & 0.29 & 13.97 & 32.31 \\  & wup & 15.06 & 31.81 & 34.45 & 57.64 & **0.27** & 0.74 & 13.97 & 32.3 \\ \cline{2-10}  & CS & & 15.11 & 32.09 & 34.77 & 58.31 & 0.15 & **0.81** & **14.39** & **33.58** \\ \cline{2-10}  & HM & path & **15.34** & 32.26 & **34.95** & **58.45** & 0.2 & **0.81** & **14.39** & **33.58** \\  & HM & lch & 15.2 & 32.22 & 34.92 & 58.4 & 0.23 & **0.81** & **14.39** & **33.58** \\  & wup & 15.13 & **32.62** & 34.77 & 58.33 & **0.27** & **0.81** & **14.39** & **33.58** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results for the proposed methods with the YOLOv2-80 detector on Visual Genome categories. higher-level categories or abstract concepts such as _whole_, _instrumentality_, and _creation_. That negative tendency causes confusion by misreferencing the surrounding information. \(\text{GT}_{\text{S}}\) outperforms the models to which other detectors are applied in most cases, which means that the contextual inference is well harmonized with the visual inference in ZSR. However, there is not a significant gap between the performance of YOLOv2-80 and that of \(\text{GT}_{\text{S}}\). In other words, our method is easy to apply practically to existing detectors without needs to provide accurate surrounding information because it can reference the potentials of classification candidates. Furthermore, it is promising to show optimized performance with a detector that stably recognizes a wide variety of categories. * Qualitative Analysis  
We also use \(\text{GT}_{\text{S}}\)-based surrounding information for qualitative evaluations to clearly analyze the effects of contextual inference.