Unsupervised Learning of Optical Flow With Non-Occlusion From Geometry-Reference-Cited by-同舟云学术

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Unsupervised Learning of Optical Flow With Non-Occlusion From Geometry

Published:2022-11 Issue:11 Volume:23 Page:20850-20859
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Wang Guangming¹^ORCID,Ren Shuaiqi¹^ORCID,Wang Hesheng¹^ORCID

Affiliation:

1. Department of Automation, Key Laboratory of System Control and Information Processing of Ministry of Education, Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai Jiao Tong University, Shanghai, China

Funder

Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Open Research Projects of Zhejiang Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9942712/09810863.pdf?arnumber=9810863

Reference43 articles.

1. Imposing Consistency for Optical Flow Estimation

2. Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion

3. Image Quality Assessment: From Error Visibility to Structural Similarity

4. A Database and Evaluation Methodology for Optical Flow

5. A Naturalistic Open Source Movie for Optical Flow Evaluation

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1. Research on traditional and deep learning strategies based on optical flow estimation - a review;Journal of King Saud University - Computer and Information Sciences;2024-04

2. ATAL: Active Learning Using Adversarial Training for Data Augmentation;IEEE Internet of Things Journal;2024-02-01

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5. SSTM: Spatiotemporal recurrent transformers for multi-frame optical flow estimation;Neurocomputing;2023-11

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