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Global contexts in images are quite valuable in image-to-image translationproblems. Conventional attention-based and graph-based models capture theglobal context to a large extent, however, these are computationally expensive.Moreover, the existing approaches are limited to only learning the pairwisesemantic relation between any two points on the image. In this paper, wepresent Latent Graph Attention (LGA) a computationally inexpensive (linear tothe number of nodes) and stable, modular framework for incorporating the globalcontext in the existing architectures, especially empowering small-scalearchitectures to give performance closer to large size architectures, thusmaking the light-weight architectures more useful for edge devices with lowercompute power and lower energy needs. LGA propagates information spatiallyusing a network of locally connected graphs, thereby facilitating to constructa semantically coherent relation between any two spatially distant points thatalso takes into account the influence of the intermediate pixels. Moreover, thedepth of the graph network can be used to adapt the extent of contextual spreadto the target dataset, thereby being able to explicitly control the addedcomputational cost. To enhance the learning mechanism of LGA, we also introducea novel contrastive loss term that helps our LGA module to couple well with theoriginal architecture at the expense of minimal additional computational load.We show that incorporating LGA improves the performance on three challengingapplications, namely transparent object segmentation, image restoration fordehazing and optical flow estimation.

Latent Graph Attention for Enhanced Spatial Context