The non-local block is a popular module for strengthening the context
modeling ability of a regular convolutional neural network. This paper first
studies the non-local block in depth, where we find that its attention
computation can be split into two terms, a whitened pairwise term accounting
for the relationship between two pixels and a unary term representing the
saliency of every pixel. We also observe that the two terms trained alone tend
to model different visual clues, e.g. the whitened pairwise term learns
within-region relationships while the unary term learns salient boundaries.
However, the two terms are tightly coupled in the non-local block, which
hinders the learning of each. Based on these findings, we present the
disentangled non-local block, where the two terms are decoupled to facilitate
learning for both terms. We demonstrate the effectiveness of the decoupled
design on various tasks, such as semantic segmentation on Cityscapes, ADE20K
and PASCAL Context, object detection on COCO, and action recognition on
Kinetics.

本文研究了非局部块，发现其关注计算可以分为两个项，其中白化的成对项和一元项分别代表两个像素之间的关系及每个像素的显著性。本文提出了解耦的非局部块，证明了该设计在不同任务中的有效性。