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.

该研究引入 FlowDA，一个用于光流估计的无监督域自适应框架，结合了无监督光流估计的概念和技术，提出了基于课程学习的自适应课程权重模块，实验证明 FlowDA 在真实场景下优于现有方法，为光流估计性能的提升提供了新的见解。