## 4 Results and Discussion
### Comparison with Other Methods  
Table 6 shows the evaluation results of the proposed approach and five high performing instance segmentation methods including Mask R-CNN on the Cityscapes test set in terms of AP and AP\({}_{50}\) criteria. The proposed methodology outperforms other frameworks and gets the best results in terms of AP and AP\({}_{50}\), which proves the efficiency of the method in Table 6. More specifically, by applying AFPM, the AP of the instance segmentation algorithm is improved by 2.1% and AP\({}_{50}\) is improved by 2.7% compared with that of the base-network. Therefore, AFPM effectively enhances the network performance. Besides, the accuracy of the proposed method not only exceeds the accuracy of the method trained on the fine set, but is also better than the accuracy of the method trained on both the fine set and coarse set.  
In Table 7, we present in detail the average precision of each category of object for different algorithms on the Cityscapes test set. The AP of the instance segmentation method based on AFPM is higher than that of the basic network in all instance classes. Besides, due to the fact that the proposed module can extract the refined features of instances in different scales and sizes, the average precision of the instance segmentation method using AFPM surpass that of other methods in multiple categories. We perform well on the rider (23.8% vs. 23.7%), car (50.2% vs. 46.9%), bus (38.5% vs. 32.2%), train (24.7% vs. 18.6%) and bicycle (16.5% vs. 16.0%) than Mask R-CNN [6]. Moreover, compared with GMIS [41] which trains on both the fine and coarse sets, our method achieves well in person, car, bus and bicycle class. Especially on the bicycle class, the average precision exceeds them by 4.6%.  
In addition, as shown in Table 8, we quantitatively display the results of FPS and AP of different instance segmentation methods when the input pixel is 2048 \(\times\) 1024. Because CBAM increases the number of parameters in the instance segmentation network and CTAM has three branches, the total inference time is lightly expanded. In a word, we add attention mechanism and branches in turn to improve the average precision of the network from 25.7% to 27.8%, and at the same time slightly decrease the inference speed of the network, that is, from 7.3 to 5.5. Compared with other algorithms in Table 8, although our algorithm does not get the maximal inference speed, we achieve a relative balance between inference time and average precision.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Training Data** & **AP (\%)** & **AP\({}_{50}\) (\%)** \\ \hline DIN [11] & fine + coarse & 23.4 & 45.2 \\ SGN [24] & fine + coarse & 25.0 & 44.9 \\ PolygonRNN++ [40] & fine & 25.5 & 45.5 \\ Mask R-CNN [6] & fine & 26.2 & 49.9 \\ GMIS [41] & fine + coarse & 27.6 & 44.6 \\ Baseline & fine & 25.7 & 47.9 \\ Baseline + AFPM & fine & 27.8 & 50.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of instance segmentation results on the Cityscapes test set.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Person** & **Rider** & **Car** & **Truck** & **Bus** & **Train** & **Motorcycle** & **Bicycle** \\ \hline DIN [11] & 20.9 & 18.4 & 31.7 & 22.8 & 31.1 & 31.0 & 19.6 & 11.7 \\ SGN [24] & 21.8 & 20.1 & 39.4 & 24.8 & 33.2 & 30.8 & 17.7 & 12.4 \\ PolygonRNN++ [40] & 29.4 & 21.8 & 48.3 & 21.1 & 32.3 & 23.7 & 13.6 & 13.6 \\ Mask R-CNN [6] & 30.5 & 23.7 & 46.9 & 22.8 & 32.2 & 18.6 & 19.1 & 16.0 \\ GMIS [41] & 29.3 & 24.1 & 42.7 & 25.4 & 37.2 & 32.9 & 17.6 & 11.9 \\ Baseline & 28.4 & 22.7 & 48.6 & 19.6 & 34.6 & 20.0 & 16.0 & 16.0 \\ Baseline + AFPM & 29.3 & 23.8 & 50.2 & 21.8 & 38.5 & 24.7 & 17.4 & 16.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of average precision (AP) (%) for each instance class on the Cityscapes test set.