Globally modeling and reasoning over relations between regions can be
beneficial for many computer vision tasks on both images and videos.
Convolutional Neural Networks (CNNs) excel at modeling local relations by
convolution operations, but they are typically inefficient at capturing global
relations between distant regions and require stacking multiple convolution
layers. In this work, we propose a new approach for reasoning globally in which
a set of features are globally aggregated over the coordinate space and then
projected to an interaction space where relational reasoning can be efficiently
computed. After reasoning, relation-aware features are distributed back to the
original coordinate space for down-stream tasks. We further present a highly
efficient instantiation of the proposed approach and introduce the Global
Reasoning unit (GloRe unit) that implements the coordinate-interaction space
mapping by weighted global pooling and weighted broadcasting, and the relation
reasoning via graph convolution on a small graph in interaction space. The
proposed GloRe unit is lightweight, end-to-end trainable and can be easily
plugged into existing CNNs for a wide range of tasks. Extensive experiments
show our GloRe unit can consistently boost the performance of state-of-the-art
backbone architectures, including ResNet, ResNeXt, SE-Net and DPN, for both 2D
and 3D CNNs, on image classification, semantic segmentation and video action
recognition task.

该研究提出了一种名为 GloRe 的全局推理单元，实现了卷积神经网络对于远距离区域之间的全局关系的建模，能够有效提升图像识别、语义分割和视频行为识别等计算机视觉任务的性能。