Liteflownet: A lightweight convolutional neural network for optical flow estimation

TW Hui, X Tang, CC Loy - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
Abstract FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow
estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper …
被引用次数：917 相关文章

LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, C Change Loy - arXiv e-prints, 2018 - ui.adsabs.harvard.edu
Abstract FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow
estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper …

LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, CC Loy - pdfs.semanticscholar.org
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation Page 1
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation T.-W. Hui …
[PDF] karlancer.com

[PDF][PDF] LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, CC Loy - karlancer.com
Abstract FlowNet2 [14], the state-of-the-art convolutional neural network (CNN) for optical
flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this …
[PDF] researchgate.net

[PDF][PDF] LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, CC Loy - researchgate.net
Abstract FlowNet2 [14], the state-of-the-art convolutional neural network (CNN) for optical
flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this …

LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, CC Loy - arXiv preprint arXiv:1805.07036, 2018 - arxiv.org
FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow estimation,
requires over 160M parameters to achieve accurate flow estimation. In this paper we present …
[PDF] thecvf.com

[PDF][PDF] LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, CC Loy - openaccess.thecvf.com
Abstract FlowNet2 [14], the state-of-the-art convolutional neural network (CNN) for optical
flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this …

LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, CC Loy - 2018 IEEE/CVF Conference on …, 2018 - ieeexplore.ieee.org
FlowNet2 [14], the state-of-the-art convolutional neural network (CNN) for optical flow
estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper …

LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, CC Loy - 2018 IEEE/CVF Conference on Computer …, 2018 - computer.org
Abstract FlowNet2 [14], the state-of-the-art convolutional neural network (CNN) for optical
flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this …

LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TW Hui, X Tang, CC Loy - 2018 - mmlab.ie.cuhk.edu.hk
Abstract FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow
estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper …