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.

本文介绍了一种计算效率高且模块化的框架 ——Latent Graph Attention（LGA），该框架能够将全局背景信息融入现有架构中，特别是能够使小型架构的性能接近大型架构，从而使轻量级架构在计算能力和能源需求较低的边缘设备中更加有效。在透明物体分割、去雾图像恢复和光流估计三个应用中，LGA 模块的引入改善了性能。

增强空间上下文的潜在图注意力

Latent Graph Attention for Enhanced Spatial Context

Major winning Convolutional Neural Networks (CNNs), such as AlexNet, VGGNet,
ResNet, GoogleNet, include tens to hundreds of millions of parameters, which
impose considerable computation and memory overhead. This limits their
practical use for training, optimization and memory efficiency. On the
contrary, light-weight architectures, being proposed to address this issue,
mainly suffer from low accuracy. These inefficiencies mostly stem from
following an ad hoc procedure. We propose a simple architecture, called
SimpleNet, based on a set of designing principles, with which we empirically
show, a well-crafted yet simple and reasonably deep architecture can perform on
par with deeper and more complex architectures. SimpleNet provides a good
tradeoff between the computation/memory efficiency and the accuracy. Our simple
13-layer architecture outperforms most of the deeper and complex architectures
to date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarks
while having 2 to 25 times fewer number of parameters and operations. This
makes it very handy for embedded systems or systems with computational and
memory limitations. We achieved state-of-the-art result on CIFAR10
outperforming several heavier architectures, near state of the art on MNIST and
competitive results on CIFAR100 and SVHN. We also outperformed the much larger
and deeper architectures such as VGGNet and popular variants of ResNets among
others on the ImageNet dataset. Models are made available at:
this https URL

提出了一种简单的卷积神经网络架构 (SimpleNet)，在保持计算和内存效率的同时，具有与更复杂的 CNN 相媲美的性能，在多个基准测试中表现出色，包括 CIFAR-10，MNIST，CIFAR-100 和 SVHN，甚至在 ImageNet 数据集中也优于许多更大更深的 CNN 模型。