Cargando…

Person Re-Identification Using Local Relation-Aware Graph Convolutional Network

Local feature extractions have been verified to be effective for person re-identification (re-ID) in recent literature. However, existing methods usually rely on extracting local features from single part of a pedestrian while neglecting the relationship of local features among different pedestrian...

Descripción completa

Detalles Bibliográficos
Autores principales:	Lian, Yu, Huang, Wenmin, Liu, Shuang, Guo, Peng, Zhang, Zhong, Durrani, Tariq S.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575217/ https://www.ncbi.nlm.nih.gov/pubmed/37836968 http://dx.doi.org/10.3390/s23198138

Ejemplares similares

Cross-Modality Person Re-Identification via Local Paired Graph Attention Network
por: Zhou, Jianglin, et al.
Publicado: (2023)

Heterogeneous feature-aware Transformer-CNN coupling network for person re-identification
por: Li, Yanchao, et al.
Publicado: (2022)

Integration of Multi-Head Self-Attention and Convolution for Person Re-Identification
por: Zhou, Yalei, et al.
Publicado: (2022)

Coarse-to-Fine Hand–Object Pose Estimation with Interaction-Aware Graph Convolutional Network
por: Zhang, Maomao, et al.
Publicado: (2021)

Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification
por: Jiang, Jinhua, et al.
Publicado: (2023)

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network
por: Lu, Changpeng, et al.
Publicado: (2023)

Person re-identification via semi-supervised adaptive graph embedding
por: Liu, Jiao, et al.
Publicado: (2022)

SGCN: A Graph Sparsifier Based on Graph Convolutional Networks
por: Li, Jiayu, et al.
Publicado: (2020)

Relational graph convolutional networks: a closer look
por: Thanapalasingam, Thiviyan, et al.
Publicado: (2022)

Dynamic Weighting Network for Person Re-Identification
por: Li, Guang, et al.
Publicado: (2023)

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
por: Berrendorf, Max, et al.
Publicado: (2020)

A deep graph convolutional neural network architecture for graph classification
por: Zhou, Yuchen, et al.
Publicado: (2023)

Gaze Gesture Recognition by Graph Convolutional Networks
por: Shi, Lei, et al.
Publicado: (2021)

Predicting anticancer hyperfoods with graph convolutional networks
por: Gonzalez, Guadalupe, et al.
Publicado: (2021)

Graph convolutional networks: a comprehensive review
por: Zhang, Si, et al.
Publicado: (2019)

A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification
por: Lin, Lan, et al.
Publicado: (2023)

Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map
por: Chen, Jianwen, et al.
Publicado: (2021)

Predicting functions of maize proteins using graph convolutional network
por: Zhou, Guangjie, et al.
Publicado: (2020)

GraphQA: protein model quality assessment using graph convolutional networks
por: Baldassarre, Federico, et al.
Publicado: (2020)

Geometric graphs from data to aid classification tasks with Graph Convolutional Networks
por: Qian, Yifan, et al.
Publicado: (2021)

Dual graph convolutional neural network for predicting chemical networks
por: Harada, Shonosuke, et al.
Publicado: (2020)

Modeling polypharmacy side effects with graph convolutional networks
por: Zitnik, Marinka, et al.
Publicado: (2018)

Intelligent malware detection based on graph convolutional network
por: Li, Shanxi, et al.
Publicado: (2021)

Modeling students’ performance using graph convolutional networks
por: Mubarak, Ahmed A., et al.
Publicado: (2022)

Single-cell classification using graph convolutional networks
por: Wang, Tianyu, et al.
Publicado: (2021)

Graph convolutional networks fusing motif-structure information
por: Wang, Bin, et al.
Publicado: (2022)

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization
por: Zhang, Xitong, et al.
Publicado: (2022)

Random projection forest initialization for graph convolutional networks
por: Alshammari, Mashaan, et al.
Publicado: (2023)

Ringer: Systematic Mining of Malicious Domains by Dynamic Graph Convolutional Network
por: Liu, Zhicheng, et al.
Publicado: (2020)

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction
por: Wang, Xianfang, et al.
Publicado: (2020)

All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks
por: Xue, Yutao, et al.
Publicado: (2022)

Self-Erasing Network for Person Re-Identification
por: Fan, Xinyue, et al.
Publicado: (2021)

Whole and Part Adaptive Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
por: Zuo, Qi, et al.
Publicado: (2020)

Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations
por: Xuan, Ping, et al.
Publicado: (2019)

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks
por: Liu, Jin, et al.
Publicado: (2020)

Resting-State Functional MRI Adaptation with Attention Graph Convolution Network for Brain Disorder Identification
por: Chu, Ying, et al.
Publicado: (2022)

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network
por: Meng, Xianghan, et al.
Publicado: (2023)

Degree-Aware Graph Neural Network Quantization
por: Fan, Ziqin, et al.
Publicado: (2023)

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia
por: Lei, Du, et al.
Publicado: (2022)

Dynamic and Static Features-Aware Recommendation with Graph Neural Networks
por: Sun, Ninghua, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_v7gnp46t7ifjqm7vjm1pqjrbam