GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer
Social relationship recognition (SRR) holds significant value in fields such as behavior analysis and intelligent social systems. However, existing methods primarily focus on modeling individual visual traits, interaction patterns, and scene-level contextual cues, often failing to capture the comple...
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| Language: | English |
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/2992 |
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| author | Dejiao Huang Menglei Xia Ruyi Chang Xiaohan Kong Shuai Guo |
| author_facet | Dejiao Huang Menglei Xia Ruyi Chang Xiaohan Kong Shuai Guo |
| author_sort | Dejiao Huang |
| collection | DOAJ |
| description | Social relationship recognition (SRR) holds significant value in fields such as behavior analysis and intelligent social systems. However, existing methods primarily focus on modeling individual visual traits, interaction patterns, and scene-level contextual cues, often failing to capture the complex dependencies among these features and the hierarchical structure of social groups, which are crucial for effective reasoning. In order to overcome these restrictions, this essay suggests a SRR model that integrates Gated Graph Neural Network (GGNN) and Transformer. The task for SRR in this model is image-based. Specifically, the purpose of a novel and robust hybrid feature extraction module is to capture individual characteristics, relative positional information, and group-level cues, which are used to construct relation nodes and group nodes. A modified GGNN is then employed to model the logical dependencies between features. Nevertheless, GGNN alone lacks the capacity to dynamically adjust feature importance, which may result in ambiguous relationship representations. The Transformer’s multi-head self-attention (MSA) mechanism is integrated to improve feature interaction modeling, allowing the model to capture global context and higher-order dependencies effectively. By fusing pairwise features, graph-structured features, and group-level information. Experimental results on public datasets such as PISC demonstrate that the proposed approach outperforms comparison models including Dual-Glance, GRM, GRRN, Graph-BERT, and SRT in terms of accuracy and mean average precision (mAP), validating its effectiveness in multi-feature representation learning and global reasoning. |
| format | Article |
| id | doaj-art-25dbc14f96b646d88548542e4eda7269 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-25dbc14f96b646d88548542e4eda72692025-08-20T03:12:15ZengMDPI AGSensors1424-82202025-05-012510299210.3390/s25102992GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based TransformerDejiao Huang0Menglei Xia1Ruyi Chang2Xiaohan Kong3Shuai Guo4College of Science, Qingdao University of Technology, Qingdao 266000, ChinaCollege of Information and Control Engineering, Qingdao University of Technology, Qingdao 266000, ChinaCollege of Science, Qingdao University of Technology, Qingdao 266000, ChinaCollege of Science, Qingdao University of Technology, Qingdao 266000, ChinaCollege of Information and Control Engineering, Qingdao University of Technology, Qingdao 266000, ChinaSocial relationship recognition (SRR) holds significant value in fields such as behavior analysis and intelligent social systems. However, existing methods primarily focus on modeling individual visual traits, interaction patterns, and scene-level contextual cues, often failing to capture the complex dependencies among these features and the hierarchical structure of social groups, which are crucial for effective reasoning. In order to overcome these restrictions, this essay suggests a SRR model that integrates Gated Graph Neural Network (GGNN) and Transformer. The task for SRR in this model is image-based. Specifically, the purpose of a novel and robust hybrid feature extraction module is to capture individual characteristics, relative positional information, and group-level cues, which are used to construct relation nodes and group nodes. A modified GGNN is then employed to model the logical dependencies between features. Nevertheless, GGNN alone lacks the capacity to dynamically adjust feature importance, which may result in ambiguous relationship representations. The Transformer’s multi-head self-attention (MSA) mechanism is integrated to improve feature interaction modeling, allowing the model to capture global context and higher-order dependencies effectively. By fusing pairwise features, graph-structured features, and group-level information. Experimental results on public datasets such as PISC demonstrate that the proposed approach outperforms comparison models including Dual-Glance, GRM, GRRN, Graph-BERT, and SRT in terms of accuracy and mean average precision (mAP), validating its effectiveness in multi-feature representation learning and global reasoning.https://www.mdpi.com/1424-8220/25/10/2992SRRGGNNtransformerfeature interaction modeling |
| spellingShingle | Dejiao Huang Menglei Xia Ruyi Chang Xiaohan Kong Shuai Guo GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer Sensors SRR GGNN transformer feature interaction modeling |
| title | GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer |
| title_full | GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer |
| title_fullStr | GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer |
| title_full_unstemmed | GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer |
| title_short | GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer |
| title_sort | gt srr a structured method for social relation recognition with ggnn based transformer |
| topic | SRR GGNN transformer feature interaction modeling |
| url | https://www.mdpi.com/1424-8220/25/10/2992 |
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