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===Learning Based Models===
These models Instead of seeking to model optical flow directly, one can train a [[machine learning]] system to estimate optical flow. Since 2015, when FlowNet<ref>{{Cite conference |last=Dosovitskiy |first=Alexey |last2=Fischer |first2=Philipp |last3=Ilg |first3=Eddy |last4=Hausser |first4=Philip |last5=Hazirbas |first5=Caner |last6=Golkov |first6=Vladimir |last7=Smagt |first7=Patrick van der |last8=Cremers |first8=Daniel |last9=Brox |first9=Thomas |date=2015 |title=FlowNet: Learning Optical Flow with Convolutional Networks |url=https://ieeexplore.ieee.org/document/7410673/ |publisher=IEEE |pages=2758–2766 |doi=10.1109/ICCV.2015.316 |isbn=978-1-4673-8391-2 | conference=2015 IEEE International Conference on Computer Vision (ICCV)}}</ref> was proposed, learning based models have been applied to optical flow and have gained prominence. Initially, these approaches were based on [[Convolutional neural network|Convolutional Neural Networks]] arranged in a [[U-Net]] architecture. However, with the advent of [[Transformer (deep learning architecture)|transformer architecture]] in 2017, transformer based have gained prominence. <ref>{{Cite journal |last=Alfarano |first=Andrea |last2=Maiano |first2=Luca |last3=Papa |first3=Lorenzo |last4=Amerini |first4=Irene |date=2024 |title=Estimating optical flow: A comprehensive review of the state of the art |url=https://linkinghub.elsevier.com/retrieve/pii/S1077314224002418 |journal=Computer Vision and Image Understanding |language=en |volume=249 |pages=104160 |doi=10.1016/j.cviu.2024.104160}}</ref>
== Uses ==
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