Attention-based motion aggregation concepts have recently shown their
usefulness in optical flow estimation, in particular when it comes to handling
occluded regions. However, due to their complexity, such concepts have been
mainly restricted to coarse-resolution single-scale approaches that fail to
provide the detailed outcome of high-resolution multi-scale networks. In this
paper, we hence propose CCMR: a high-resolution coarse-to-fine approach that
leverages attention-based motion grouping concepts to multi-scale optical flow
estimation. CCMR relies on a hierarchical two-step attention-based
context-motion grouping strategy that first computes global multi-scale context
features and then uses them to guide the actual motion grouping. As we iterate
both steps over all coarse-to-fine scales, we adapt cross covariance image
transformers to allow for an efficient realization while maintaining
scale-dependent properties. Experiments and ablations demonstrate that our
efforts of combining multi-scale and attention-based concepts pay off. By
providing highly detailed flow fields with strong improvements in both occluded
and non-occluded regions, our CCMR approach not only outperforms both the
corresponding single-scale attention-based and multi-scale attention-free
baselines by up to 23.0% and 21.6%, respectively, it also achieves
state-of-the-art results, ranking first on KITTI 2015 and second on MPI Sintel
Clean and Final. Code and trained models are available at
this https URL /CCMR.

提出了使用注意力机制的运动聚合概念在光流估计中的应用，通过高分辨率多尺度网络实现了高分辨率的精细运动估计，比单一尺度和多尺度注意力自由方法分别提高了 23.0% 和 21.6%，取得了 KITTI 2015 排名第一和 MPI Sintel Clean 和 Final 排名第二的最先进结果。