# Efficient Semantic Segmentation Using Multi-Path Decoder
## 5 Conclusions  
In this work, we present a novel network, MPDNet, for fast and accurate semantic segmentation. The network has an encoder-decoder structure to encode the rich contextual information and recover the object boundaries. We use a powerful lateral connection to fuse the semantic information in deep layers with the spatial information in early layers. These connections help feature maps in deep layers with encoding abstract contextual information regardless of low-level details and small objects. Compared with various dilated architectures, this design considerably decreases computational complexity while achieving competitive results. Considering the difference between shape information and other information, we use two decoders to recover shape information and other information, respectively. The edge decoder affects the main decoder by adding the edge loss to the main loss.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Single Scale Test mIoU(\%)/PA(\%)** & **Multi Scale Test mIoU(\%)/PA(\%)** & **SS FLOPs@1 Mpx** \\ \hline PSPNet [27] & 41.96/80.64 & 43.29/81.39 & 722G+ \\ UPerNet [22] & 42.00/80.79 & 42.66/81.01 & 370.74G \\ DSSPN [30] & 43.68/81.13 & - \\ PSANet [31] & 42.75/80.71 & 43.77/81.51 & 722G+ \\ OCNet [32] & 4.54/- & 722G+ \\ MPDNet & 43.14/80.91 & 44.01/81.44 & 246.9G \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison to state-of-the-art on the validation set of ADE20K. “-” represents that it is not reported in the related work.  
Figure 3: Visual improvements on ADE20K val set. MPDNet produces more accurate and detailed results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Encoder** & **RB** & **Edge Decoder** & **Multi-Path Decoder** & **MS** & **mIoU(\%)** & **PA(\%)** \\ \hline ResNet-50 & & & & & 38.88 & 78.34 \\ ResNet-50 & ✓ & & & & 39.56 & 79.65 \\ ResNet-50 & ✓ & ✓ & & & 40.98 & 79.93 \\ ResNet-50 & ✓ & ✓ & ✓ & & 42.06 & 80.42 \\ ResNet-101 & ✓ & ✓ & ✓ & & 43.14 & 80.91 \\ ResNet-101 & ✓ & ✓ & ✓ & ✓ & 44.01 & 81.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Investigation of MPDNet with different settings on the validation set of ADE20K.  
There may be a more effective way to use the edge decoder to improve the main decoder. Meanwhile, we divide the main decoder to four paths to extract different type features. In each path, the enriched feature maps are concatenated to produce a high-resolution feature map. We supervise each branch of the main decoder by calculating loss for them. As an FPN-based model, MPDNet is relatively faster than many state-of-the-art models. Our experimental results show that our proposed model achieves good results on semantic segmentation benchmark Cityscapes (76.7%) with only 119 GFLOPs.  
Same as other semantic segmentation models, it requires a large number of finely annotated images to train our model. How to train an effective model with a small number of annotated images is still a problem to be solved. Due to the lack of high-resolution representations, it is still difficult for models to segment small objects, especially for models with large output stride. A model that can segment small objects well will greatly improve the benchmark. Meanwhile, we do not use information between frames in video segmentation. We look forward to a weakly supervised model for semantic video segmentation.  
All authors contribute equally. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China (Nos. 11590770, 61650202, 11590772,11722437, 61671442) and IACAS Young Elite Researcher Project (No. Y854141431).  
The authors declare there is no conflicts of interest regarding the publication of this paper.