Suchergebnisse - Encoder-Decoder ConvLSTM

Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation von 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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EDDA-ConvLSTM: Encoder-Decoder Dual Attention ConvLSTM for Moroccan Coastal Sea Surface Temperature Prediction von Zahrae El Azhary, Fatima, Minaoui, Khalid

“… This study presents an advanced encoder-decoder dual attention convolutional long short-term memory (ConvLSTM …”
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An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting von Wang, Cheng, Xue, Kaiyu, Shi, Chuang

“… This study introduces two optimized models based on the ConvLSTM cell with an encoder-decoder structure to enhance forecasting performance …”
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Predicting 360° Video Saliency: A ConvLSTM Encoder-Decoder Network With Spatio-Temporal Consistency von 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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Effective Multi-Step PM2.5 and PM10 Air Quality Forecasting Using Bidirectional ConvLSTM Encoder-Decoder With STA Mechanism von 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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PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction von 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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Advancing spatiotemporal forecasts of CO2 plume migration using deep learning networks with transfer learning and interpretation analysis von 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 von Imak, Andaç, Çelebi, Adalet, Polat, Onur, Türkoğlu, Muammer, Şengür, Abdulkadir

“… Objective Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, …”
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Forecasting Stock Market Using Machine Learning Approach Encoder-Decoder ConvLSTM von Iqbal, Khurum, Hassan, Ali, Hassan, Syed Shah Mir Ul, Iqbal, Shuaib, Aslam, Faheem, Mughal, Khurrum S

“… The goal of this study is to create a hybrid Deep Learning model (Encoder-Decoder ConvLSTM …”
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Prediction of maize growth stages based on deep learning von 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 von 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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Local and Long-range Convolutional LSTM Network: A novel multi-step wind speed prediction approach for modeling local and long-range spatial correlations based on ConvLSTM von Yu, Mei, Tao, Boan, Li, Xuewei, Liu, Zhiqiang, Xiong, Wei

“… Deformable Convolution V2 and Coordinate Attention for multi-step spatiotemporal wind speed prediction. A ConvLSTM encoder …”
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Multi-Scale Attention and Encoder-Decoder Network for Video Saliency Object Detection von Bi, Hongbo, Zhu, Huihui, Yang, Lina, Wu, Ranwan

“… — In recent years, video saliency object detection has received more and more attention, and many excellent algorithms have been proposed. In the paper, we …”
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Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM von 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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Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid von 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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New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast von 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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Semantic segmentation of oblique UAV video based on ConvLSTM in complex urban area von 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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ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction von Li, Yalan, Deng, Haiming, Xiao, Jian, Li, Bin, Han, Tao, Huang, Jianquan, Liu, Haijun

“… Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective …”
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Dual Convolutional LSTM Network for Referring Image Segmentation von 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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Deep unsupervised multi-modal fusion network for detecting driver distraction von Zhang, Yuxin, Chen, Yiqiang, Gao, Chenlong

“… •A state-of-the-art, unsupervised, end-to-end method to detect driver distraction.•Different network architectures to perform embedding subnetworks for …”
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