Human Activity Recognition and Location Based on Temporal Analysis by Hongjin Ding, Faming Gong, Wenjuan Gong, Xiangbing Yuan, Yuhui Ma

“…For this work, we used a multilayer convolutional neural network (CNN) to extract features. …”
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Temporal-Aware Transformer Approach for Violence Activity Recognition by Rajdeep Chatterjee, Ritabrata Roy Choudhury, Mahendra Kumar Gourisaria, Sreejata Banerjee, Soumik Dey, Manoj Sahni, Ernesto Leon-Castro

“…In the first approach, Convolutional Neural Networks (CNN) and bidirectional long-short-term memory (BiLSTM) networks are combined, where MobileNetV2 is used for spatial feature extraction and BiLSTM for temporal pattern recognition, achieving an accuracy of 95.6%. …”
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Comparative Study of Hybrid Deep Learning Models for Kannada Sign Language Recognition by Gurusiddappa Hugar, Ramesh M. Kagalkar, Abhijit Das

“…This study presents a novel hybrid deep learning architecture that synergistically combines convolutional neural networks (CNNs), hand keypoints (HKPs), long short-term memory (LSTM) networks, and transformers to achieve robust spatial-temporal-contextual learning for KSL recognition. …”
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xLSTM Interaction Multilevel SSM-Assisted Decoding Network for Remote Sensing Image Change Detection by Chunpeng Wu, Shuli Cheng, Anyu Du, Liejun Wang, Wenbin Tang

“…With the advancements of convolutional neural networks (CNNs) and Transformers in deep learning, the accuracy of RSCD has significantly improved. …”
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Multitask semantic change detection guided by spatiotemporal semantic interaction by Yinqing Wang, Liangjun Zhao, Yueming Hu, Hui Dai, Yuanyang Zhang

“…To further enhance detection performance, a dynamic depthwise separable convolution is designed in the CTIM module, which can adaptively adjust convolution kernels to more precisely capture change features in different regions of the image. …”
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TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification by Ruimin Peng, Changming Zhao, Jun Jiang, Guangtao Kuang, Yuqi Cui, Yifan Xu, Hao Du, Jianbo Shao, Dongrui Wu

“…A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. …”
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Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning by Shilong Liu, Stéphane Virally, Gabriel Demontigny, Patrick Cusson, Denis V. Seletskiy

“…Here, we propose and demonstrate how a physics-embedded convolutional neural network that embeds spectro-temporal correlations can circumvent such challenges, resulting in faster convergence and reduced noise sensitivity. …”
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Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention by Yash Jakhmola, Madhurima Panja, Nitish Kumar Mishra, Kripabandhu Ghosh, Uttam Kumar, Tanujit Chakraborty

“…While graph convolutional networks and multi-head attention mechanisms have been widely adopted in this field, they often fail to accurately model dynamic temporal patterns and effectively differentiate noise from signals in traffic datasets, leading to potential overfitting. …”
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Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment by Neha Tarigopula, Sandrine Tornay, Ozge Mercanoglu Sincan, Richard Bowden, Mathew Magimai.-Doss

“…To address this limitation, we leverage and analyze the spatio-temporal representations from Inflated 3D Convolutional Networks (I3D) and integrate them into the KL-HMM framework to assess sign language videos on both manual and non-manual components. …”
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Two-Stage Video Violence Detection Framework Using GMFlow and CBAM-Enhanced ResNet3D by Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla, Mahmoud Salaheldin Kasem, Hyun-Soo Kang

“…The proposed approach effectively combines GMFlow-generated optical flow with deep 3D convolutional networks, providing robust and efficient detection of violence in videos.…”
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Time-Series Forecasting Method Based on Hierarchical Spatio-Temporal Attention Mechanism by Zhiguo Xiao, Junli Liu, Xinyao Cao, Ke Wang, Dongni Li, Qian Liu

“…This study innovatively proposes a Spatio-Temporal Attention-Enhanced Network (TSEBG). Breaking through traditional structural designs, the model employs a Squeeze-and-Excitation Network (SENet) to reconstruct the convolutional layers of the Temporal Convolutional Network (TCN), strengthening the feature expression of key time steps through dynamic channel weight allocation to address the redundancy issue of traditional causal convolutions in local pattern capture. …”
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Orbital Behavior Intention Recognition for Space Non-Cooperative Targets Under Multiple Constraints by Yuwen Chen, Xiang Zhang, Wenhe Liao, Guoning Wei, Shuhui Fan

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Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features by Gang Li, Chen Lin, Yupeng Li

“…Then, in order to effectively consider the spatial meteorological distribution characteristics of regional power stations and the temporal characteristics of historical power, a parallel prediction network architecture of a convolutional neural network (CNN) and long short-term memory (LSTM) is designed. …”
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STDNet: Improved lip reading via short-term temporal dependency modeling by Xiaoer Wu, Zhenhua Tan, Ziwei Cheng, Yuran Ru

“…In particular, we designed a local–temporal block, which aggregates interframe differences, strengthening the relationship between various local lip regions through multiscale convolution. …”
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Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer by Nobuko Irie, Naoki Takeda, Yorifumi Satou, Kimi Araki, Masahiro Ono

“…We have developed a convolutional neural network-based method that incorporates image conversion and class-specific feature visualisation for class-specific feature identification at the single-cell level. …”
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Satellite Image Time-Series Classification with Inception-Enhanced Temporal Attention Encoder by Zheng Zhang, Weixiong Zhang, Yu Meng, Zhitao Zhao, Ping Tang, Hongyi Li

“…In this study, we propose a one-branch IncepTAE network to extract local and global hybrid temporal attention simultaneously and congruously for fine-grained satellite image time series (SITS) classification. …”
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Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis by Zehua Yu, Xianwei Zheng, Zhulun Yang, Bowen Lu, Xutao Li, Maxian Fu

“…Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. …”
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Comparative Analysis of Attention Mechanisms in Densely Connected Network for Network Traffic Prediction by Myeongjun Oh, Sung Oh, Jongkyung Im, Myungho Kim, Joung-Sik Kim, Ji-Yeon Park, Na-Rae Yi, Sung-Ho Bae

“…Recently, STDenseNet (SpatioTemporal Densely connected convolutional Network) showed remarkable performance in predicting network traffic by leveraging the inductive bias of convolution layers. …”
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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
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STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis by Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Boon Xian Chai

“…However, current approaches relying on Convolutional Neural Networks (CNNs) for Euclidean data and Graph Convolutional Networks (GCNs) for non-Euclidean structures struggle to simultaneously capture heterogeneous data properties and complex spatio-temporal dependencies. …”
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