# Relevancy Between Objects Based on Common Sense for Semantic Segmentation
## 3 Experiments
The training set and test set consist of 9K and 1K images, respectively. OCRNet was also used for the dataset to compare the challenges of the COCO-Stuff dataset and the ADE20K dataset. Meanwhile, PSPNet and DeepLabV3, two classical image semantic segmentation models, were selected for the experiment. The core contribution of PSPNet was to propose the pyramid pooling module, which can integrate different scale context information, improve the ability to obtain global feature information and increase the expression of the model. DeepLabV3 proposed the atrous spatial pyramid pooling module, which can tap different-scale convolutional features and encode global image feature information to enhance semantic segmentation. Although they were proposed in 2017, it is also appropriate to test the effects of the post-processing methods. The experimental results from the COCO-Stuff dataset are listed in Table 4. As we can see from Table 4, the COCO-Stuff dataset is more challenging than the ADE20K dataset by the performance of OCRNet. After a combination of the two post-processing methods, the original results of the three models are improved to 38.93%, 38.95% and 40.63%, respectively. They are all improved by more than 1%, and their overall performance gains are higher than the former experiment. Through the comparative analysis of Tables 3 and 4, the post-processing method has a more significant effect on the more difficult datasets, which is also in line with our common sense. The final result of the post-processing method proposed in this paper seriously relies on the original mIoU of the deep learning model. Several representative models have been selected to conduct experiments. There may be models that are more suitable for the post-processing method; that is, the gain of post-processing will be greater. Because we have proved the effectiveness of the post-processing method, this paper no longer performs more repeated experiments to verify other models. Based on analyzing the results of the above two groups of experiments and the deep understanding of the principle of post-processing, the post-processing method will have a greater effect gain for processing complex images or blurry images in real application scenarios. Complex images generally include many categories, and the correlation between categories is strong, so the effect will be better after application processing. Due to the shooting angle or the influence of other objective factors, blurred images have some unclear areas, and the effect gain of the post-processing method will be greater for processing such images. In order to visually demonstrate the effect of the post-processing method, some samples from the ADE20K dataset are presented in the form of comparison, as shown in Figure 3. The first column represents the original prediction result of the deep learning \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Orig. mIoU** & **The 1 st M.** & **The 1+2 nd M.** \\ \hline PSPNet & 37.26 & 38.09 & 38.93 \\ DeepLabV3 & 37.3 & 38.12 & 38.95 \\ OCRNet & 39.5 & 39.94 & 40.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results on the COCO-Stuff dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Orig. mIoU** & **The 1 st M.** & **The 1+2 nd M.** \\ \hline UperNet50 & 40.7 & 41.11 & 42.07 \\ OCRNet & 43 & 43.23 & 44.09 \\ SETR-MLA & 48.64 & 48.83 & 49.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results on the ADE20K dataset. methods, the second column represents the result after the post-processing method, and the third column represents the annotation results in Figure 3. It is easy to see that window, river, bed and light in the examples are corrected by the post-processing method. Specifically, the large blue area on the window in the first image in the first row is caused by the wrong prediction of a deep learning model in Figure 3, which is corrected in the second image.