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Paper Abstract and Keywords
Presentation 2025-06-06 14:15
A study on lightweight and highly accurate CNN-based video prediction models
Masaki Nakahara, Jiro Katto (Waseda Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, many video prediction models have been proposed. In this study, we propose to replace the InceptionU module of SimVP, a video prediction model composed only of CNN, with another network. The proposed method learns temporal evolution using another network and predict future frames using those features. We performed a quantitative evaluation using objective evaluation indices for the predicted frames and confirmed that the proposed method is superior to existing models.
Keyword (in Japanese) (See Japanese page) 
(in English) Frame Prediction / Frame Extrapolation / SimVP / MogaNet / PredRNN / ConvLSTM / /  
Reference Info. ITE Tech. Rep.
Paper #  
Date of Issue  
ISSN Online edition: ISSN 2424-1970
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Conference Information
Committee IST ME IEICE-IE IEICE-BioX IEICE-SIP IEICE-MI  
Conference Date 2025-06-05 - 2025-06-06 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Topics (in English)  
Paper Information
Registration To IEICE-IE 
Conference Code 2025-06-IST-ME-IE-BioX-SIP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on lightweight and highly accurate CNN-based video prediction models 
Sub Title (in English)  
Keyword(1) Frame Prediction  
Keyword(2) Frame Extrapolation  
Keyword(3) SimVP  
Keyword(4) MogaNet  
Keyword(5) PredRNN  
Keyword(6) ConvLSTM  
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1st Author's Name Masaki Nakahara  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Jiro Katto  
2nd Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2025-06-06 14:15:00 
Presentation Time 25 minutes 
Registration for IEICE-IE 
Paper #  
Volume (vol) vol.49 
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#Pages  
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