# Relevancy Between Objects Based on Common Sense for Semantic Segmentation
## 3 Experiments  
In this section, the experimental results for the two post-processing methods for the semantic segmentation task are introduced. Considering the adequacy of the experiment, two different public datasets were selected. Let us introduce one of them first. The ADE20K semantic segmentation dataset contains 150 categories. It is divided into 20,120, 2000, 3000 images for training, validation and testing. ADE20K is the most popular dataset in the field of image semantic segmentation and one of the most difficult publicly available datasets; it contains many categories and is very close to the scene distribution in the real world. We conduct several experiments on three different logits output by three different deep learning models. They are UPerNet, OCRNet and SETR. UPerNet aimed to parse visual concepts across scene categories, objects, parts, materials and textures from images. UPerNet50 achieved 40.7% mean IoU on the ADE20K validation set with single-scale inference. OCRNet added a cross-attention module to its encoder-decoder architecture. OCRNet achieved 43% mean IoU on the ADE20K validation set with single-scale inference. SETR introduced a sequence-to-sequence prediction framework as an alternative perspective for semantic segmentation. SETR-MLA achieved 48.64% mean IoU on the ADE20K validation set with single-scale inference. The results of the ablation study on OCRNet are listed in Table 2 to illustrate the effect and difference between the first method and the second method. If the average confidence of a patch is below 0.35, it means that the model is very unconfident in the classification result. In this case, the first candidate obtained from the reference vector has a very high probability of being the correct category, which is why the first method is effective. When we apply the first method to all connected components, the result decreases instead. When the threshold is set between 0.35 and 0.7, the second method is more effective than the first method. It improves the result to 43.86%. When the threshold is between 0.7 and 1, the second method has no influence on the result, and the first method has a side effect for the first method is a stronger condition than the second. Overall, we first calculate the average confidence for each connected component. Then according to the results obtained, we determine which post-processing method to use or not. The experimental results on three representative models are listed in Table 3. The original results of the three models are all from the implementations by [31]. The experiments in Table 2 have shown that the first method is more effective than the second method when the threshold is below 0.35 and has a side effect when the threshold is above 0.35. According to this finding, the first method is applied to all connected components with an average confidence of less than 0.35, and all connected components with average confidence above 0.35 are not processed. Therefore, it brings the results of the three models to 41.11%, 43.23% and 48.83%, respectively. If we use the first post-processing method on connected components with average confidence less than 0.35, use the second method on connected components with average confidence between 0.35 and 0.7 and connected components with average confidence above 0.7 are not processed, the results of the three models will be \begin{table}
\begin{tabular}{c c c} \hline \hline
**Avg. Conf.** & **The 1 st Method** & **The 2 nd Method** \\ \hline
[0, 0.35] & 43.23 & 43.15 \\ (0.35, 0.7] & 42.88 & 43.86 \\ (0.7, 1] & 42.64 & 43 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study on OCRNet. improved to 42.07%, 44.09% and 49.09%, respectively. Obviously, the combination of the first method and the second method is more effective than any single method. In order to further verify the effectiveness of the two post-processing methods, additional datasets and two other methods were selected for experiments. The COCO-Stuff dataset is a challenging scene-parsing dataset that contains 171 semantic classes.