Optical flow, or the estimation of motion fields from image sequences, is one
of the fundamental problems in computer vision. Unlike most pixel-wise tasks
that aim at achieving consistent representations of the same category, optical
flow raises extra demands for obtaining local discrimination and smoothness,
which yet is not fully explored by existing approaches. In this paper, we push
Gaussian Attention (GA) into the optical flow models to accentuate local
properties during representation learning and enforce the motion affinity
during matching. Specifically, we introduce a novel Gaussian-Constrained Layer
(GCL) which can be easily plugged into existing Transformer blocks to highlight
the local neighborhood that contains fine-grained structural information.
Moreover, for reliable motion analysis, we provide a new Gaussian-Guided
Attention Module (GGAM) which not only inherits properties from Gaussian
distribution to instinctively revolve around the neighbor fields of each point
but also is empowered to put the emphasis on contextually related regions
during matching. Our fully-equipped model, namely Gaussian Attention Flow
network (GAFlow), naturally incorporates a series of novel Gaussian-based
modules into the conventional optical flow framework for reliable motion
analysis. Extensive experiments on standard optical flow datasets consistently
demonstrate the exceptional performance of the proposed approach in terms of
both generalization ability evaluation and online benchmark testing. Code is
available at this https URL

本文通过将高斯注意力引入光流模型，以强调表示学习过程中的局部特性并在匹配过程中强制实施运动相关性，提出了一种全新的高斯约束层（GCL）和高斯引导注意模块（GGAM）。GAFlow 网络是一个完整的模型，将这些高斯基于模块自然地融入传统的光流框架中，用于可靠的运动分析。在标准光流数据集上进行的广泛实验一致证明了所提方法在泛化能力评估和在线基准测试方面的优异性能。