  Global contexts in images are quite valuable in image-to-image translation
problems. Conventional attention-based and graph-based models capture the
global context to a large extent, however, these are computationally expensive.
Moreover, the existing approaches are limited to only learning the pairwise
semantic relation between any two points on the image. In this paper, we
present Latent Graph Attention (LGA) a computationally inexpensive (linear to
the number of nodes) and stable, modular framework for incorporating the global
context in the existing architectures, especially empowering small-scale
architectures to give performance closer to large size architectures, thus
making the light-weight architectures more useful for edge devices with lower
compute power and lower energy needs. LGA propagates information spatially
using a network of locally connected graphs, thereby facilitating to construct
a semantically coherent relation between any two spatially distant points that
also takes into account the influence of the intermediate pixels. Moreover, the
depth of the graph network can be used to adapt the extent of contextual spread
to the target dataset, thereby being able to explicitly control the added
computational cost. To enhance the learning mechanism of LGA, we also introduce
a novel contrastive loss term that helps our LGA module to couple well with the
original architecture at the expense of minimal additional computational load.
We show that incorporating LGA improves the performance on three challenging
applications, namely transparent object segmentation, image restoration for
dehazing and optical flow estimation.
