Unsupervised Learning Optical Flow in Multi-frame Dynamic Environment Using Temporal Dynamic Modeling. (arXiv:2304.07159v1 [cs.CV])

For visual estimation of optical flow, a crucial function for many vision
tasks, unsupervised learning, using the supervision of view synthesis has
emerged as a promising alternative to supervised methods, since ground-truth
flow is not readily available in many cases. However, unsupervised learning is
likely to be unstable when pixel tracking is lost due to occlusion and motion
blur, or the pixel matching is impaired due to variation in image content and
spatial structure over time. In natural environments, dynamic …

arxiv cases dynamic environment environments flow function image modeling natural optical flow pixel supervision synthesis temporal tracking unsupervised unsupervised learning vision