Disentangling Architecture and Training for Optical Flow

Disentangling Architecture and Training for Optical Flow

21 March 2022

Charles Herrmann

Michael Rubinstein

David Fleet

William T. Freeman

Papers citing "Disentangling Architecture and Training for Optical Flow"

9 / 9 papers shown

Title
Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesis Michal Geyer Omer Tov Linyi Jin Richard Tucker Inbar Mosseri Tali Dekel Noah Snavely DiffM VGen 88 0 0 30 Apr 2025
Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion Yuxiang Huang Yuhao Chen John S. Zelek 20 1 0 02 May 2024
Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume Gangwei Xu Shujun Chen Hao Jia Miaojie Feng Xin Yang 33 5 0 06 Dec 2023
GAFlow: Incorporating Gaussian Attention into Optical Flow Ao Luo Fan Yang Xin Li Lang Nie Chunyu Lin Haoqiang Fan Shuaicheng Liu 23 22 0 28 Sep 2023
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation Hyun-Joo Jung Zhuo Hui Lei Luo Haitao Yang Feng Liu S. Yoo Rakesh Ranjan D. Demandolx 3DPC 6 13 0 29 Mar 2023
ResNet strikes back: An improved training procedure in timm Ross Wightman Hugo Touvron Hervé Jégou AI4TS 194 477 0 01 Oct 2021
High-Resolution Optical Flow from 1D Attention and Correlation Haofei Xu Jiaolong Yang Jianfei Cai Juyong Zhang Xin Tong 66 72 0 28 Apr 2021
Learnable Cost Volume Using the Cayley Representation Taihong Xiao Jinwei Yuan Deqing Sun Qifei Wang Xinyu Zhang Kehan Xu Ming-Hsuan Yang 29 12 0 21 Jul 2020
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 204 1,275 0 04 Dec 2018