## 5 Conclusions  
This paper presents the novel concept of latent graph attention (LGA). Attention in CNNs is either local, non-local, or global. However, the concept of LGA progressively includes the global context in a chained fashion without significant computational complexity by propagating information across networks of locally connected graphs. Chained attention makes LGA particularly suitable for the challenging problem of transparent object segmentation. Furthermore, a novel divergence loss function is incorporated to help the LGA module perform learning in a collaborative manner with the parent architecture and \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Original** & **Reduced** & **Reduced with LGA** \\ \hline SSIM & 0.8994 & 0.8482 & 0.8663 \\ PSNR & 22.56 & 18.89 & 20.17 \\ Parameters (\(\times 10^{6}\)) & 8.851 & 0.685 & 0.687 \\ FLOPS (\(\times 10^{9}\)) & 348.49 & 87.44 & 87.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation study 4: Ablation on BPPNet [24] for dehazing of the I-HAZE [7] dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of LGA Layers** & **0** & **1** & **2** & **4** & **8** \\ \hline mIoU (\%) & 36.9 & 43.1 & 43.9 & 44.5 & 41.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation for no. of LGA layers on the Trans10 Kv2 (squeeze) dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Performance (\(\uparrow\))**} & \multicolumn{2}{c}{**Extra Parameters (10\({}^{9}\)) (\(\downarrow\))**} & \multicolumn{4}{c}{**Extra FLOPS (10\({}^{6}\)) (\(\downarrow\))**} \\ \cline{2-9}
**MODEL** & **mIoU** & **Accuracy** & \begin{tabular}{c} **Conv.** \\ **Channel** \\ **Resizing** \\ \end{tabular} & \begin{tabular}{c} **Attention** \\ **Module** \\ **Module** \\ \end{tabular} & **Total** & \begin{tabular}{c} **Conv.** \\ **Channel** \\ **Resizing** \\ \end{tabular} & \begin{tabular}{c} **Attention** \\ **Resizing** \\ \end{tabular} & \
\begin{tabular}{c} **Mother Conv.** \\ **Propagation** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Operations** \\ \end{tabular} \\ \hline CCNet [8] & 42.8 & 79.6 & 2359 & 327 & 2686 & 4832 & 150 & 670 & 5652 \\ LGA & 45.8 & 81.8 & 66 & 67 & 132 & 67 & 5 & 68 & 140 \\ LGA small & 44.6 & 79.6 & 8 & 9 & 17 & 8 & 5 & 9 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ablation study for performance and efficiency comparison of LGA vs. CCNet [8] with a SqueezeNet encoder on the Trans10 Kv2 dataset [25]. Here, LGA indicates simple convolutions, and LGA small indicates group convolutions. The original mIoU and accuracy of SqueezeNet [34] without any LGA or CCNet module were 41.5 and 76.2, respectively. enhance the performance of the original architecture with a minor additional computational load. The computational efficiency of LGA and its contribution to performance improvement and versatility were demonstrated through multiple studies. The LGA module was employed in different architectures to solve three challenging image-to-image translation problems, namely transparent object segmentation, image dehazing, and unsupervised optical flow estimation. It was shown to be an effective mechanism to learn global context and contribute this information to the parent architecture in which it is included as a module. We hope that LGA will find use in many challenging applications and across a wide variety of architectures where it is not practical or straightforward to incorporate attention or global context mechanisms. In particular, we expect that LGA will be a powerful tool to upgrade the performance of edge devices with very limited memory and computational resources.