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Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition [article]

Jin Ye, Junjun He, Xiaojiang Peng, Wenhao Wu, Yu Qiao
2020 arXiv   pre-print
To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image.  ...  Recent studies often exploit Graph Convolutional Network (GCN) to model label dependencies to improve recognition accuracy for multi-label image recognition.  ...  This work is partially supported by National Natural Science Foundation of China (U1813218, U1713208), Science and Technology Service Network Initiative of Chinese Academy of Sciences (KFJ-STS-QYZX-092  ... 
arXiv:2012.02994v1 fatcat:arphfwobvvbbdmd3sclhbg4nxm

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TMM 2021 3752-3767 Anisotropic Graph Convolutional Network for Semi-Supervised Learning. High Dynamic Range Imaging.  ...  ., +, TMM 2021 443-453 Disentangling, Embedding and Ranking Label Cues for Multi-Label Image Recognition.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 1915-1928 Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks.  ...  ., +, TIP 2020 1815-1826 Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Guest Editorial Introduction to the Special Section on Intelligent Visual Content Analysis and Understanding

Hongliang Li, Lu Fang, Tianzhu Zhang
2020 IEEE transactions on circuits and systems for video technology (Print)  
"Video dialog via multi-grained convolutional self-attention context multi-modal networks," by Gu et al., employs a multigrained convolutional self-attention context network to learn the joint representations  ...  "Multi-exposure decomposition-fusion model for high dynamic range image saliency detection," by Wang et al., presents a two-stage framework to estimate the saliency map toward high dynamic range (HDR)  ... 
doi:10.1109/tcsvt.2020.3031416 fatcat:gpwbmydqbza5lddatxcfcidwcq

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TIP 2021 1116-1129 Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition.  ...  ., +, TIP 2021 6544-6556 Learning Dynamic Relationships for Facial Expression Recognition Based on Graph Convolutional Network.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Table of Contents

2021 IEEE transactions on multimedia  
Wang Image/Video/Graphics Analysis and Synthesis A Multi-Stream Graph Convolutional Networks-Hidden Conditional Random Field Model for Skeleton-Based Action Recognition . . . . . . . . . . . . . . . .  ...  Déforges Deep Learning for Multimedia Processing Disentangling, Embedding and Ranking Label Cues for Multi-Label Image Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tmm.2021.3132246 fatcat:el7u2udtybddrpbl5gxkvfricy

Adaptive Attention Memory Graph Convolutional Networks for Skeleton-Based Action Recognition

Di Liu, Hui Xu, Jianzhong Wang, Yinghua Lu, Jun Kong, Miao Qi
2021 Sensors  
Graph Convolutional Networks (GCNs) have attracted a lot of attention and shown remarkable performance for action recognition in recent years.  ...  In this work, we propose a novel Adaptive Attention Memory Graph Convolutional Networks (AAM-GCN) for human action recognition using skeleton data.  ...  memory module to develop the Adaptive Attention Memory Graph Convolutional Network (AAM-GCN) for skeleton action recognition.  ... 
doi:10.3390/s21206761 pmid:34695972 pmcid:PMC8538327 fatcat:qvnxnvdshndxjprt5orjdy7dgm

Multi-scale Mixed Dense Graph Convolution Network for Skeleton-based Action Recognition

Hailun Xia, Xinkai Gao
2021 IEEE Access  
INDEX TERMS Dense graph convolution, spatial and temporal attention module, multi-scale mixed temporal convolution, skeleton-based action recognition.  ...  In this paper, we design a multi-scale mixed dense graph convolutional network (MMDGCN) to overcome both shortcomings.  ...  CONCLUSION In this paper, we propose a multi-scale mixed dense graph convolution network for skeleton-based action recognition.  ... 
doi:10.1109/access.2020.3049029 fatcat:xlmmcsmp3vbnvj422wctwwjiei

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
., +, TCSVT Feb. 2020 509-519 Convolutional codes Attention-Driven Loss for Anomaly Detection in Video Surveillance.  ...  ., +, TCSVT Dec. 2020 4540-4553 Video Dialog via Multi-Grained Convolutional Self-Attention Context Multi-Modal Networks.  ...  A Memory-Efficient Hardware Architecture for Connected Component Labeling in Embedded System.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

2021 Index IEEE Signal Processing Letters Vol. 28

2021 IEEE Signal Processing Letters  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, LSP 2021 66-70 Multi-Label Classification of Fundus Images With Graph Convolutional Network and Self-Supervised Learning.  ...  Parashar, D., +, LSP 2021 66-70 Multi-Label Classification of Fundus Images With Graph Convolutional Network and Self-Supervised Learning.  ... 
doi:10.1109/lsp.2022.3145253 fatcat:a3xqvok75vgepcckwnhh2mty74

Table of Contents

2021 IEEE Signal Processing Letters  
Wang Multi-Label Classification of Fundus Images With Graph Convolutional Network and Self-Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Jin Deep Superpixel Convolutional Network for Image Recognition . . . . . . . . . . ...X. Zeng, W. Wu, G. Tian, F. Li, and Y.  ... 
doi:10.1109/lsp.2021.3134549 fatcat:m6obtl7k7zdqvd62eo3c4tptfy

S-MAT: Semantic-Driven Masked Attention Transformer for Multi-Label Aerial Image Classification

Hongjun Wu, Cheng Xu, Hongzhe Liu
2022 Sensors  
To solve this problem, we propose S-MAT, a Semantic-driven Masked Attention Transformer for multi-label aerial scene image classification.  ...  Therefore, our method achieves CF1s of 89.21%, 90.90%, and 88.31% on three multi-label aerial scene image classification benchmark datasets: UC-Merced Multi-label, AID Multi-label, and MLRSNet, respectively  ...  [38] use high-level spatial features to build an updatable graph via a Dynamic Graph Convolutional Network (D-GCN) module.  ... 
doi:10.3390/s22145433 pmid:35891109 pmcid:PMC9317133 fatcat:yu5fzvhtq5bxjf7sb46y5vjkje

Deep learning‐based action recognition with 3D skeleton: A survey

Yuling Xing, Jia Zhu
2021 CAAI Transactions on Intelligence Technology  
In this survey, we first introduce the development process of 3D skeleton-data action recognition and the classification of graph convolutional network, then introduce the commonly used NTU RGB + D and  ...  Action recognition based on 3D skeleton data has attracted much attention due to its wide application, and it is one of the most popular research topics in computer vision.  ...  Recently, the graph convolutional network (GCN) based method was proposed and attract attention owing to its achievement of high performance.  ... 
doi:10.1049/cit2.12014 fatcat:77vtqpozjnfdrj3qodcbjzgjmq

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 Li et al., ''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  ...  ., ''MsCoa: Multi-step co-attention model for multi-label classification,'' proposes an improved multistep multiclassification model to mitigate the phenomenon of error prediction, label repetition, and  ... 
doi:10.1109/access.2020.3043060 fatcat:qbqk5f4ojvadlazhk2mc343sra

Table of Contents

2022 IEEE journal of biomedical and health informatics  
Zhang 1472 SPECIAL ISSUE ON AI-DRIVEN INFORMATICS, SENSING, IMAGING AND BIG DATA ANALYTICS FOR FIGHTING THE COVID-19 PANDEMIC PCXRNet: Pneumonia Diagnosis From Chest X-Ray Images Using Condense Attention  ...  Haoyong 1749 Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jbhi.2022.3159171 fatcat:n4v32gnznnht7mcjyakuhwenky
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