Latent Graph Attention for Spatial Context in Light-Weight Networks: Multi-Domain Applications in Visual Perception Tasks

Abstract

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, existing approaches are limited to only learning the pairwise semantic relation between any two points in 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 existing architectures. This framework particularly empowers small-scale architectures to achieve performance closer to that of large architectures, 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 the construction of 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 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 performance in three challenging applications, namely transparent object segmentation, image restoration for dehazing and optical flow estimation.

Keywords

• graph attention
• light-weight network
• convolutional neural network
• transparent object segmentation
• neural networks
• optical flow estimation