Please use this identifier to cite or link to this item:
Title: Image-based social relation recognition using graph neural network
Authors: Gao, Jianjun
Keywords: Engineering::Electrical and electronic engineering
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Gao, J. (2021). Image-based social relation recognition using graph neural network. Master's thesis, Nanyang Technological University, Singapore.
Abstract: Social relation, which indicates how people are connected in society, is an essential part of our social life. With the boom of social media, data like pictures, videos, and texts, become available and can be used for social relation recognition (SRR). Meantime, the advancement of computing infrastructure and computer vision research in recent years has made it possible for computers to process these kinds of data to recognize social relations in our life. SRR is a complex topic as social relations among humans diverse a lot. Because of Convolutional Neural Network (CNN) and Graph Neural Network (GNN), it has become possible for a machine to recognize social relations in an acceptable condition. SRR problems were mostly solved by feature extraction and graph reasoning process which depend on CNN and GNN respectively. In this work, the proposed SRR was based on the Interpersonal Relation benchmark dataset [1]. Also, following existing work, we extracted features from multi-scale views from images and reasoned by two-directional graphs with Gated Recurrent Unit (GRU) attention mechanism. The results showed that the proposed work surpasses the state-of-the-art work on the Interpersonal Relation dataset by about 10% in balanced accuracy.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
  Restricted Access
11.18 MBAdobe PDFView/Open

Page view(s)

Updated on May 24, 2022


Updated on May 24, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.