Collecting real-world optical flow datasets is a formidable challenge due to the high cost of labeling. A shortage of datasets significantly constrains the real-world performance of optical flow models. Building virtual datasets that resemble real scenarios offers a potential solution for performance enhancement, yet a domain gap separates virtual and real datasets. This paper introduces FlowDA, an unsupervised domain adaptive (UDA) framework for optical flow estimation. FlowDA employs a UDA architecture based on mean-teacher and integrates concepts and techniques in unsupervised optical flow estimation. Furthermore, an Adaptive Curriculum Weighting (ACW) module based on curriculum learning is proposed to enhance the training effectiveness. Experimental outcomes demonstrate that our FlowDA outperforms state-of-the-art unsupervised optical flow estimation method SMURF by 21.6%, real optical flow dataset generation method MPI-Flow by 27.8%, and optical flow estimation adaptive method FlowSupervisor by 30.9%, offering novel insights for enhancing the performance of optical flow estimation in real-world scenarios. The code will be open-sourced after the publication of this paper.

翻译：由于标注成本高昂，收集真实世界光流数据集极具挑战性。数据集的匮乏严重制约了光流模型在真实场景中的性能。构建与真实场景相似的虚拟数据集为性能提升提供了潜在解决方案，但虚拟与真实数据集之间存在域间隙。本文提出FlowDA——一种面向光流估计的无监督域自适应（UDA）框架。FlowDA采用基于平均教师（mean-teacher）的UDA架构，并融合了无监督光流估计中的概念与技术。此外，我们提出基于课程学习的自适应课程加权（ACW）模块以增强训练效果。实验结果表明，我们的FlowDA相比当前最先进的无监督光流估计方法SMURF提升21.6%，相比真实光流数据集生成方法MPI-Flow提升27.8%，相比光流估计自适应方法FlowSupervisor提升30.9%，为提升真实场景光流估计性能提供了新思路。本论文发表后相关代码将开源。