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YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection [article]

Alexander Wong, Mahmoud Famuori, Mohammad Javad Shafiee, Francis Li, Brendan Chwyl, Jonathan Chung
2019 arXiv   pre-print
In this study, we introduce YOLO Nano, a highly compact deep convolutional neural network for the task of object detection.  ...  As such, there has been growing research interest in the design of efficient deep neural network architectures catered for edge and mobile usage.  ...  Fully-connected Attention Macroarchitecture The second notable observation about the YOLO Nano network architecture is the strategic introduction of light-weight fully-connected attention (FCA) within  ... 
arXiv:1910.01271v1 fatcat:dntmfgejdrhuvcavsnr5wndcce

Papers by Title

2021 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)  
in English Chinese Translation Defect Detection on Wafer Map Using Efficient Convolutional Neural Network Degradation Level Estimation of Road Structures via Attention Branch Network with Text Data Degradation  ...  Circuits for Human Activity Recognition Modified Attention Spatial Convolution Model for Skin Lesion Segmentation Multi-language Text Detection and Recognition Based on Deep Learning Multi-threaded System  ... 
doi:10.1109/icce-tw52618.2021.9602951 fatcat:x5phgfzzl5cafcxul53thv5w7e

Learning Where to Look for COVID-19 Growth: Multivariate Analysis of COVID-19 Cases Over Time using Explainable Convolution-LSTM [article]

Novanto Yudistira, Sutiman Bambang Sumitro, Alberth Nahas, Nelly Florida Riama
2021 medRxiv   pre-print
Multivariate analysis via visual attribution of explainable Convolution-LSTM is utilized to see high contributing factors responsible for the growth of daily COVID-19 cases.  ...  However, it may not be significant in closed spaces like workspace and areas with the intensive human-to-human transmission, especially in densely populated areas.  ...  s multivariate multi-factory PV energy prediction, [24] which uses a two-stage convolutional neural network (CNN).  ... 
doi:10.1101/2021.02.13.21251683 fatcat:mhfxry3n2zfpjctyqxtgojyviu

Predicting Human Mobility via Long Short-Term Patterns

Jianwei Chen, Jianbo Li, Ying Li
2020 CMES - Computer Modeling in Engineering & Sciences  
Predicting human mobility has great significance in Location based Social Network applications, while it is challenging due to the impact of historical mobility patterns and current trajectories.  ...  Motivated by recent success of Convolutional Neural Network (CNN)-based methods, we propose a Union ConvGRU (UCG) Net, which can capture long short-term patterns of historical trajectories and sequential  ...  Related Work Human mobility prediction has drawn great attentions for decades, existing research efforts can be categorized into conventional and neural network-based methods.  ... 
doi:10.32604/cmes.2020.010240 fatcat:dn2tr67pr5hjhhzdcn2nsh4g6e

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
mobility, location based social network, crime analysis, and neuroscience.  ...  Recently, with the advances of deep learning techniques, deep leaning models such as convolutional neural network (CNN) and recurrent neural network (RNN) have enjoyed considerable success in various machine  ...  (e.g. traffic flow prediction), human mobility (e.g. human trajectory pattern mining), etc.  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy

Research on Intelligent Estimation Method of Human Moving Target Pose Based on Adaptive Attention Mechanism

Meishuang Ding, Jing Zhao
2022 Wireless Communications and Mobile Computing  
Human decision-making patterns are imitated to evaluate the effectiveness of regional attention in real time.  ...  Second, with the target frame obtained from each frame, the pose estimation algorithm finds the key points of human body, enabling the human body pose optimization strategy to solve the crossover problem  ...  Acknowledgments is research was supported in part by the Key Humanities and Social Science Research Project in Anhui Province (SK2020A0955) and Anhui School-Enterprise Cooperation Practice Education Base  ... 
doi:10.1155/2022/2141194 doaj:2af57dcadc604491aba9ab8d4a2dcfb2 fatcat:azp3gmhz2faibehkdunizaln4u

A survey on next location prediction techniques, applications, and challenges

Ayele Gobezie Chekol, Marta Sintayehu Fufa
2022 EURASIP Journal on Wireless Communications and Networking  
It is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information.  ...  AbstractNext location prediction has recently gained great attention from researchers due to its importance in different application areas.  ...  PeriodicMove is a neural attention model based on a graph neural network for human mobility recovery from lengthy and sparse trajectories.  ... 
doi:10.1186/s13638-022-02114-6 fatcat:s2ixs3ftibaobighbik6ikgfce

2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29

2019 IEEE transactions on circuits and systems for video technology (Print)  
Tu, Z., +, TCSVT May 2019 1423-1437 Sharp Attention Network via Adaptive Sampling for Person Re-Identification.  ...  Sheng, H., +, TCSVT Dec. 2019 3660-3672 Sharp Attention Network via Adaptive Sampling for Person Re-Identifica- tion.  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

Table of Contents

2021 IEEE transactions on multimedia  
Li Subjective and Objective Quality Assessment and User Experience Beyond Vision: A Multimodal Recurrent Attention Convolutional Neural Network for Unified Image Aesthetic Prediction Tasks . . . . . .  ...  Hou Multimedia Search and Retrieval Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tmm.2021.3132246 fatcat:el7u2udtybddrpbl5gxkvfricy

Table of Contents

2019 IEEE transactions on multimedia  
Hancock 300 Unsupervised Learning of Human Pose Distance Metric via Sparsity Locality Preserving Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Pu 351 (Contents Continued on Back Cover) (Contents Continued from Front Cover) Multimodal Human-Machine Interfaces and Interaction Attention-Deep Learning for Multimedia Processing Gradient Prior-Aided  ... 
doi:10.1109/tmm.2019.2892660 fatcat:35egnbnnofbkpnmrujdvtpjj7m

DeepSpace: An Online Deep Learning Framework for Mobile Big Data to Understand Human Mobility Patterns [article]

Xi Ouyang, Chaoyun Zhang, Pan Zhou, Hao Jiang, Shimin Gong
2018 arXiv   pre-print
In this paper, we analyze mobile data to predict human trajectories in order to understand human mobility pattern via a deep-structure model called "DeepSpace".  ...  Furthermore, we develop the vanilla convolutional neural network (CNN) to be an online learning system, which can deal with the continuous mobile data stream.  ...  When predicting human trajectories via mobile big data, the call detail records (CDRs) from the mobile cellular network have been widely used by many researchers.  ... 
arXiv:1610.07009v2 fatcat:wi3aqymwvvafhpug3xchj66v5u

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao, Shirui Pan, Jia Wu, Huaiyu Wan, Huizhi Liang, Haishuai Wang, Huawei Shen
2020 IEEE Access  
The article by Zhao et al., ''Exploring deep spectrum representations via attention-based recurrent and convolutional neural networks for speech emotion recognition,'' develops a model which leverages  ...  ., ''MV-GCN: Multi-view graph convolutional networks for link prediction,'' proposes a novel multiview graph convolutional neural network (MV-GCN) model based on the Matrix Completion method by simultaneously  ... 
doi:10.1109/access.2020.3043060 fatcat:qbqk5f4ojvadlazhk2mc343sra

2020 Index IEEE Transactions on Cognitive and Developmental Systems Vol. 12

2020 IEEE Transactions on Cognitive and Developmental Systems  
Zhong, S., +, TCDS Sept. 2020 601-617 Convolutional neural nets DeepFeat: A Bottom-Up and Top-Down Saliency Model Based on Deep Features of Convolutional Neural Networks.  ...  ., +, TCDS Sept. 2020 461-473 DeepFeat: A Bottom-Up and Top-Down Saliency Model Based on Deep Features of Convolutional Neural Networks.  ... 
doi:10.1109/tcds.2020.3044690 fatcat:yfo6c366aramfdltqegqyqphbq

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
Virtu- 1503-1515 414 A Multi-Stream Graph Convolutional Networks-Hidden Conditional Ran-sio, J.J., +, TMM 2021 2273-2285 Understanding More About Human and Machine Attention in Deep Neural Predicting the  ...  ., +, TMM 2021 3059-3072 Beyond Vision: A Multimodal Recurrent Attention Convolutional Neural Network for Unified Image Aesthetic Prediction Tasks.  ...  ., Low-Rank Pairwise Align- ment Bilinear Network For Few-Shot Fine-Grained Image Classification; TMM 2021 1666-1680 Huang, H., see 1855 -1867 Huang, H., see Jiang, X., TMM 2021 2602-2613 Huang, J.,  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Scanning the Issue

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
Four key components, including a lightweight baseline network with atrous convolution and attention (LBN-AA), the distinctive atrous spatial pyramid pooling (DASPP), a spatial detail-preserving network  ...  A real-time high-performance deep convolutional neural network-based method is proposed for robust semantic segmentation of urban street scenes.  ...  Zheng A novel convolutional embedding model (CEM) is proposed to predict next locations using traffic trajectory data, via modeling the relative ordering of locations with a 1-D convolution.  ... 
doi:10.1109/tits.2021.3079675 fatcat:mtssvivuebcplifv6bw233jbkq
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