  Despite significant progress in deep learning-based optical flow methods,
accurately estimating large displacements and repetitive patterns remains a
challenge. The limitations of local features and similarity search patterns
used in these algorithms contribute to this issue. Additionally, some existing
methods suffer from slow runtime and excessive graphic memory consumption. To
address these problems, this paper proposes a novel approach based on the RAFT
framework. The proposed Attention-based Feature Localization (AFL) approach
incorporates the attention mechanism to handle global feature extraction and
address repetitive patterns. It introduces an operator for matching pixels with
corresponding counterparts in the second frame and assigning accurate flow
values. Furthermore, an Amorphous Lookup Operator (ALO) is proposed to enhance
convergence speed and improve RAFTs ability to handle large displacements by
reducing data redundancy in its search operator and expanding the search space
for similarity extraction. The proposed method, Efficient RAFT
(Ef-RAFT),achieves significant improvements of 10% on the Sintel dataset and 5%
on the KITTI dataset over RAFT. Remarkably, these enhancements are attained
with a modest 33% reduction in speed and a mere 13% increase in memory usage.
The code is available at: https://github.com/n3slami/Ef-RAFT
