Search Results - Spatial-Temporal Autoencoder

Stacked Spatial-Temporal Autoencoder for Quality Prediction in Industrial Processes by Yan, Feng, Yang, Chunjie, Zhang, Xinmin

“… Therefore, in this paper, a stacked spatial-temporal autoencoder (S 2 TAE) is proposed to enhance the representation learning capability for soft sensor modeling by taking the spatial-temporal correlations into consideration…”
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Batch process quality prediction based on denoising autoencoder-spatial temporal convolutional attention mechanism fusion network: Batch process quality prediction based on denoising autoencoder-spatial temporal convolutional attention mechanism fusion network by Zhang, Yan, Cao, Jie, Zhao, Xiaoqiang, Hui, Yongyong

“… to prediction performance. Therefore, a denoising autoencoder-Spatial Temporal Convolution Attention Fusion Network (DAE-STCAFN…”
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Efficient identification of Alzheimer’s brain dynamics with Spatial-Temporal Autoencoder: A deep learning approach for diagnosing brain disorders by Wu, Lingyun, Zhao, Quanfa, Liu, Jing, Yu, Haitao

“…•A Spatial-Temporal Autoencoder (STAE) framework was designed to estimate latent factors of EEG via unsupervised learning…”
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A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting by Ma, Long, Huang, Ling, Shi, Huifeng

“…–temporal correlation between wind turbines. In this paper, based on variational Bayesian inference, we propose a novel spatial…”
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Multivariate time series anomaly detection with variational autoencoder and spatial–temporal graph network by Guan, Siwei, He, Zhiwei, Ma, Shenhui, Gao, Mingyu

“…–temporal graph networks and variational autoencoder (VAE). It employs…”
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Multi-modal hybrid modeling strategy based on Gaussian Mixture Variational Autoencoder and spatial–temporal attention: Application to industrial process prediction by Peng, Haifei, Long, Jian, Huang, Cheng, Wei, Shibo, Ye, Zhencheng

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Spatial–Temporal Heatmap Masked Autoencoder for Skeleton-Based Action Recognition by Bian, Cunling, Yang, Yang, Wang, Tao, Lu, Weigang

“… In this work, we propose the Spatial–Temporal Heatmap Masked Autoencoder (STH-MAE), a novel self-supervised framework tailored for skeleton-based action recognition…”
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Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction by D’Angelo, Gianni, Palmieri, Francesco

“… For this purpose, a novel autoencoder-based deep neural network architecture…”
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A Spatial-Temporal Variational Graph Attention Autoencoder Using Interactive Information for Fault Detection in Complex Industrial Processes by Lv, Mingjie, Li, Yonggang, Liang, Huiping, Sun, Bei, Yang, Chunhua, Gui, Weihua

“… A spatial-temporal variational graph attention autoencoder (STVGATE) using interactive information is proposed for fault detection, which aims to effectively capture the spatial and temporal features of the interconnected…”
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Robust Spatial-Temporal Autoencoder for Unsupervised Anomaly Detection of Unmanned Aerial Vehicle With Flight Data by Jiang, Guoqian, Nan, Pengcheng, Zhang, Jingchao, Li, Yingwei, Li, Xiaoli

“… Specifically, we designed a new robust spatial-temporal autoencoder (RSTAE) model based on the temporal convolution network (TCN…”
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Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes by Li, Nanjun, Chang, Faliang, Liu, Chunsheng

“…". In this paper, we propose a cuboid-patch-based method characterized by a cascade of classifiers called a spatial-temporal cascade autoencoder (ST-CaAE…”
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Masked Autoencoders for Spatial-Temporal Relationship in Video-Based Group Activity Recognition by Yadav, Rajeshwar, Halder, Raju, Banda, Gourinath

“…Group Activity Recognition (GAR) is a challenging problem involving several intricacies. The core of GAR lies in delving into spatiotemporal features to…”
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STAR: A Spatial-Temporal Autoencoder for EEG Restoration in Emotion Recognition by Yin, Hao-Long, Zheng, Wei-Long, Lu, Bao-Liang

“… To overcome these challenges, we introduce the Spatial-Temporal Autoencoder for EEG Restoration (STAR…”
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Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations by Ahmed, Adeel, Saleem, Khalid, Khalid, Osman, Gao, Jiechao, Rashid, Umer

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Multimodal Multi-View Spectral-Spatial-Temporal Masked Autoencoder for Self-Supervised Emotion Recognition by Gao, Pengxuan, Liu, Tianyu, Liu, Jia-Wen, Lu, Bao-Liang, Zheng, Wei-Long

“… In this paper, we propose a Multimodal Multi-view Spectral-Spatial-Temporal Masked Autoencoder (Multimodal MV-SSTMA…”
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Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network by Li, Nanjun, Chang, Faliang, Liu, Chunsheng

“… The global component is utilized to compute local component. The local component sequences are then input to our network for capturing normal spatial-temporal motion patterns of human skeleton…”
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Sensor Spoofing Detection On Autonomous Vehicle Using Channel-spatial-temporal Attention Based Autoencoder Network by Zhou, Man, Han, Lansheng

“… In response, this paper proposes a channel-spatial-temporal attention-based autoencoder network to detect sensor spoofing attacks on autonomous vehicles…”
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Spatial-temporal hierarchical decoupled masked autoencoder: A self-supervised learning framework for electrocardiogram by Wei, Xiaoyang, Li, Zhiyuan, Tian, Yuanyuan, Wang, Mengxiao, Jin, Yanrui, Ding, Weiping, Liu, Chengliang

“… In this paper, we propose a Spatial-Temporal Hierarchical Decoupled Masked Autoencoder (STHD-MAE). This framework decouples ECG into isolated leads or time steps…”
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Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition by Wu, Wenhan, Hua, Yilei, Zheng, Ce, Wu, Shiqian, Chen, Chen, Lu, Aidong

“… Inspired by the MAE [1], we propose a spatial-temporal masked autoencoder framework for self-supervised 3D skeleton-based action recognition (SkeletonMAE…”
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Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos by Herzog, S, Wörgötter, F, Parlitz, U

“… The algorithm employs a convolutional autoencoder for dimension reduction and feature extraction combined with a probabilistic prediction scheme operating in the feature space, which consists…”
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