Optical flow is a classical task that is important to the vision community.
Classical optical flow estimation uses two frames as input, whilst some recent
methods consider multiple frames to explicitly model long-range information.
The former ones limit their ability to fully leverage temporal coherence along
the video sequence; and the latter ones incur heavy computational overhead,
typically not possible for real-time flow estimation. Some multi-frame-based
approaches even necessitate unseen future frames for current estimation,
compromising real-time applicability in safety-critical scenarios. To this end,
we present MemFlow, a real-time method for optical flow estimation and
prediction with memory. Our method enables memory read-out and update modules
for aggregating historical motion information in real-time. Furthermore, we
integrate resolution-adaptive re-scaling to accommodate diverse video
resolutions. Besides, our approach seamlessly extends to the future prediction
of optical flow based on past observations. Leveraging effective historical
motion aggregation, our method outperforms VideoFlow with fewer parameters and
faster inference speed on Sintel and KITTI-15 datasets in terms of
generalization performance. At the time of submission, MemFlow also leads in
performance on the 1080p Spring dataset. Codes and models will be available at:
this https URL

MemFlow 是一种实时的光流估计和预测方法，通过使用内存中的历史运动信息实现光流的估计和预测，并通过适应不同视频分辨率来提高性能。