Search Results - Encoder-Decoder ConvLSTM networks

Predicting 360° Video Saliency: A ConvLSTM Encoder-Decoder Network With Spatio-Temporal Consistency by Wan, Zhaolin, Qin, Han, Xiong, Ruiqin, Li, Zhiyang, Fan, Xiaopeng, Zhao, Debin

“… In this study, we propose a novel spatio-temporal consistency generative network for 360° VSP. A dual-stream encoder-decoder architecture is adopted to process the forward and backward frame sequences of 360…”
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Advancing spatiotemporal forecasts of CO2 plume migration using deep learning networks with transfer learning and interpretation analysis by Fan, Ming, Wang, Hongsheng, Zhang, Jing, Hosseini, Seyyed A., Lu, Dan

“… In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED…”
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ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images by Imak, Andaç, Çelebi, Adalet, Polat, Onur, Türkoğlu, Muammer, Şengür, Abdulkadir

“… In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks…”
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PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction by Zhang, Deyu, Wang, Changying, Huang, Baoxiang, Ren, Jing, Zhao, Junli, Hou, Guojia

“… To address these challenges, we propose an innovative encoder–decoder pyramid dilated convolutional long short-term memory network (DED-ConvLSTM…”
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Prediction of maize growth stages based on deep learning by Yue, Yang, Li, Jin-Hai, Fan, Li-Feng, Zhang, Li-Li, Zhao, Peng-Fei, Zhou, Qiao, Wang, Nan, Wang, Zhong-Yi, Huang, Lan, Dong, Xue-Hui

“…•ConvLSTM encoder-decoder model can forecast daily weather factors correctly.•Hybrid model and data-driven model can predict maize growth stages…”
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ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model by Xia, Guozhen, Zhang, Fubin, Wang, Cheng, Zhou, Chen

“…In this paper, we proposed an innovative encoder‐decoder structure with a convolution long short‐term memory (ED‐ConvLSTM…”
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Dual Convolutional LSTM Network for Referring Image Segmentation by Ye, Linwei, Liu, Zhi, Wang, Yang

“… Our model consists of an encoder network and a decoder network, where ConvLSTM is used in both encoder and decoder networks to capture spatial and sequential information…”
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Semantic segmentation of oblique UAV video based on ConvLSTM in complex urban area by Majidizadeh, Abbas, Hasani, Hadiseh, Jafari, Marzieh

“… . , require accurate and efficient segmentation algorithms. The proposed method implements a deep learning framework combining SegNet encoder-decoder architecture and convolutional long short-term memory (ConvLSTM…”
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Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid by Dorado Rueda, Fernando, Durán Suárez, Jaime, del Real Torres, Alejandro

“… To this end, the authors propose an encoder-decoder architecture inspired by WaveNet, a deep generative model initially designed by Google DeepMind for raw audio waveforms…”
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Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation by Kim, Jeongdae, Choo, Hyunseung, Jeong, Jongpil

“… However, ConvLSTM has limitations in capturing long-term temporal dependencies. To solve this problem, this study proposes an encoder…”
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Energy Demand Load Forecasting for Electric Vehicle Charging Stations Network based on ConvLSTM and BiConvLSTM Architectures by Mohammad, Faisal, Kang, Dong-Ki, Ahmed, Mohamed A., Kim, Young-Chon

“…) as a new entity is one of the most important challenging tasks. The implementation of the EVCS network infrastructure should facilitate the adoption of the spatiotemporal electricity demand for EVs…”
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Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM by Li, Liangchao, Liu, Haijun, Le, Huijun, Yuan, Jing, Shan, Weifeng, Han, Ying, Yuan, Guoming, Cui, Chunjie, Wang, Junling

“… Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules…”
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Effective Multi-Step PM2.5 and PM10 Air Quality Forecasting Using Bidirectional ConvLSTM Encoder-Decoder With STA Mechanism by Lakshmi, S., Krishnamoorthy, A.

“…Effective prediction of PM2.5 and PM10 levels is essential for preserving public health and informing governmental actions. Nevertheless, the unpredictable…”
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ED‐AttConvLSTM: An Ionospheric TEC Map Prediction Model Using Adaptive Weighted Spatiotemporal Features by Li, Liangchao, Liu, Haijun, Le, Huijun, Yuan, Jing, Wang, Haoran, Chen, Yi, Shan, Weifeng, Ma, Li, Cui, Chunjie

“…‐AttConvLSTM, using a Convolutional Long Short‐Term Memory (ConvLSTM) network and attention mechanism based on encoder‐decoder structure…”
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New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast by de Paulo, M. C. M., Marques, H. A., Feitosa, R. Q., Ferreira, M. P.

“…) of the days before the prediction period. We proposed modifications to the encoder–decoder convolutional long short-term memory (ED-ConvLSTM…”
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Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model by Yang, Jiayue, Huang, Wengeng, Xia, Guozhen, Zhou, Chen, Chen, Yanhong

“… The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM…”
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A Robust Change Detection Methodology for Flood Events Using SAR Images by Al-Saad, Mina, Aburaed, Nour, Zitouni, M. Sami, Alkhatib, Mohammed Q., Almansoori, Saeed, Al Ahmad, Hussain

“…) that follows encoder-decoder scheme. By introducing Bidirectional Convolutional LSTM (ConvLSTM) layers into its architecture, the proposed Temporal-Spatial Encoder-Decoder Network…”
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A spatiotemporal correlation deep learning network for brain penumbra disease by Liu, Liangliang, Zhang, Pei, Liang, Gongbo, Xiong, Shufeng, Wang, Jianxin, Zheng, Guang

“… Here, we propose an encoder-decoder network (ConvLSTM-Net) with a specifically convolutional long short-term memory skip connection to extract the spatiotemporal correlations of features of adjacent slices in a non-linear manner…”
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3D convolutional long short-term encoder-decoder network for moving object segmentation by Turker, Anil, Eksioglu, Ender

“… While traditional approaches to MOS rely on hand-crafted features or background modeling, deep learning methods using Convolution Neural Networks (CNNs…”
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Ship Roll Motion Prediction Using ConvLSTM with Attention Mechanism by Li, Weizhong, Ren, Junsheng

“… In response to this challenge, a short-term roll motion prediction model based on the convolutional long-short-term memory (ConvLSTM…”
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