PH-GCN: Person Retrieval with Part-based Hierarchical Graph Convolutional Network

2021 ◽  
pp. 1-1
Author(s):  
Bo Jiang ◽  
Xixi Wang ◽  
Aihua Zheng ◽  
Jin Tang ◽  
Bin Luo
Keyword(s):  
Convolutional Network ◽  
Hierarchical Graph

Structure-Aware Multi-scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition

2021 ◽  
pp. 293-304
Author(s):  
Changxiang He ◽  
Shuting Liu ◽  
Ying Zhao ◽  
Xiaofei Qin ◽  
Jiayuan Zeng ◽  
...  
Keyword(s):  
Action Recognition ◽  
Convolutional Network ◽  
Multi Scale ◽  
Hierarchical Graph

scholarly journals Predicting compound-protein interaction using hierarchical graph convolutional networks

2021 ◽  
Author(s):  
Danh Bui-Thi ◽  
Emmanuel Rivière ◽  
Pieter Meysman ◽  
Kris Laukens
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Small Molecules ◽  
Convolutional Neural Networks ◽  
Protein Interactions ◽  
Computational Method ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Generic Models ◽  
Hierarchical Graph

AbstractMotivationConvolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction.ResultsExperiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction.Availability and implementationhttps://github.com/banhdzui/cpi_hgcn.git

scholarly journals Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network

2020 ◽  
Author(s):  
Hongjie Cai ◽  
Yaofeng Tu ◽  
Xiangsheng Zhou ◽  
Jianfei Yu ◽  
Rui Xia
Keyword(s):  
Sentiment Analysis ◽  
Convolutional Network ◽  
Hierarchical Graph

scholarly journals Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification

2019 ◽  
Author(s):  
Fenyu Hu ◽  
Yanqiao Zhu ◽  
Shu Wu ◽  
Liang Wang ◽  
Tieniu Tan
Keyword(s):  
Receptive Field ◽  
Performance Improvement ◽  
Global Information ◽  
Convolutional Network ◽  
Network Mining ◽  
Convolutional Networks ◽  
Hierarchical Graph ◽  
Empirical Performance ◽  
Classification Tasks ◽  
Node Classification

Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation are usually shallow and lack the “graph pooling” mechanism, which prevents the model from obtaining adequate global information. In order to increase the receptive field, we propose a novel deep Hierarchical Graph Convolutional Network (H-GCN) for semi-supervised node classification. H-GCN first repeatedly aggregates structurally similar nodes to hyper-nodes and then refines the coarsened graph to the original to restore the representation for each node. Instead of merely aggregating one- or two-hop neighborhood information, the proposed coarsening procedure enlarges the receptive field for each node, hence more global information can be captured. The proposed H-GCN model shows strong empirical performance on various public benchmark graph datasets, outperforming state-of-the-art methods and acquiring up to 5.9% performance improvement in terms of accuracy. In addition, when only a few labeled samples are provided, our model gains substantial improvements.

scholarly journals Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Information Aggregation ◽  
Semantic Segmentation ◽  
Spatial Correlations ◽  
Convolutional Network ◽  
Object Based ◽  
Graph Neural Networks ◽  
Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

scholarly journals SACN: A Novel Rotating Face Detector Based on Architecture Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 558
Author(s):  
Anping Song ◽  
Xiaokang Xu ◽  
Xinyi Zhai
Keyword(s):  
Face Detection ◽  
Human Face ◽  
Angle Error ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Data Set ◽  
Practical Applications ◽  
Model Size ◽  
Average Angle ◽  
Face Detector

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.

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