FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
Author:
Affiliation:
1. CUHK MMLab
2. NVIDIA AI Technology Center
3. SenseTime Research
4. Tsinghua University
Funder
National Key R&D Program of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10203037/10203050/10204766.pdf?arnumber=10204766
Reference82 articles.
1. FlowNet: Learning Optical Flow with Convolutional Networks
2. AutoFlow: Learning a Better Training Set for Optical Flow
3. An image is worth 16×16 words: Transformers for image recognition at scale;dosovitskiy;ArXiv Preprint,2020
4. A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them
5. Convmae: Masked convolution meets masked autoencoders;gao;ArXiv Preprint,2022
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