GMAMflow: learning optical flow through global multi-scale attention matching

Optical flow estimation is essential for various computer vision applications, including autonomous vehicles, object tracking, and frame interpolation. However, enhancing the accuracy of optical flow algorithms while optimizing their structural complexity continues to be a formidable challenge. This paper presents GMAMflow, a groundbreaking optical flow estimation model that leverages a global multi-scale attention mechanism. Our enhancement of the existing EMA allows the network to more effectively capture intricate details across diverse scales within complex imagery. The architectural refinements to the GMflow framework are specifically designed to elevate the learning process of optical flow. Comprehensive experimental validation using the Sintel dataset demonstrates that our approach achieves higher accuracy and outperforms competing methods. These results signify a significant stride in optical flow research, offering not only theoretical contributions but also practical implications for the advancement of technology in associated domains.