Detail Preserving Residual Feature Pyramid Modules for Optical Flow-Reference-Cited by-同舟云学术

Detail Preserving Residual Feature Pyramid Modules for Optical Flow

Published:2022-01 Issue: Volume: Page:
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Container-title:2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Long Libo¹,Lang Jochen¹

Affiliation:

1. University of Ottawa,EECS

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9706406/9706408/09707071.pdf?arnumber=9707071

Reference46 articles.

1. Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation

2. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

3. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Medical Image Computing and Computer-Assisted Intervention (MICCAI) volume 9351 of LNCS,2015

4. ResFPN: Residual skip connections in multi-resolution feature pyramid networks for accurate dense pixel matching;rishav;International Conference on Pattern Recognition,2020

5. A Fusion Approach for Multi-Frame Optical Flow Estimation

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1. MS-RAFT+: High Resolution Multi-Scale RAFT;International Journal of Computer Vision;2023-12-18

2. GCA-Net: A Global Context Aggregation Network for Effective Optical Flow;IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society;2023-10-16

3. DCVNet: Dilated Cost Volume Networks for Fast Optical Flow;2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2023-01

4. Multi-Scale Raft: Combining Hierarchical Concepts for Learning-Based Optical Flow Estimation;2022 IEEE International Conference on Image Processing (ICIP);2022-10-16