Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation

Video prediction, which is the task of predicting future video frames based on past observations, remains a challenging problem because of the complexity and high dimensionality of spatiotemporal dynamics. To address the problems associated with spatiotemporal prediction, which is an important decis...

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Vydané v:Applied sciences Ročník 14; číslo 23; s. 11315
Hlavní autori: Kim, Jeongdae, Choo, Hyunseung, Jeong, Jongpil
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.12.2024
Predmet:
Computational linguistics
Datasets
encoder–decoder
Forecasts and trends
Language processing
Natural language interfaces
Neural networks
Performance evaluation
SA-ConvLSTM
self-attention memory module
spatiotemporal
video prediction
ISSN:2076-3417, 2076-3417
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Shrnutí:Video prediction, which is the task of predicting future video frames based on past observations, remains a challenging problem because of the complexity and high dimensionality of spatiotemporal dynamics. To address the problems associated with spatiotemporal prediction, which is an important decision-making tool in various fields, several deep learning models have been proposed. Convolutional long short-term memory (ConvLSTM) can capture space and time simultaneously and has shown excellent performance in various applications, such as image and video prediction, object detection, and semantic segmentation. However, ConvLSTM has limitations in capturing long-term temporal dependencies. To solve this problem, this study proposes an encoder–decoder structure using self-attention ConvLSTM (SA-ConvLSTM), which retains the advantages of ConvLSTM and effectively captures the long-range dependencies through the self-attention mechanism. The effectiveness of the encoder–decoder structure using SA-ConvLSTM was validated through experiments on the MovingMNIST, KTH dataset.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app142311315