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.

In recent years, multimodal social relation recognition has become a critical task in the fields of computer vision and natural language processing. However, existing research still faces key gaps, particularly in effectively aligning image features with linguistic features to improve recognition accuracy. This paper proposes a social relation recognition method based on multimodal feature fusion and validates the crucial role of cross-modal alignment mechanisms in enhancing recognition accuracy. Our approach leverages large language models to meticulously extract event structures from textual descriptions, capturing key elements such as emotional states, scenarios, and relationships, while employing convolutional neural networks to extract deep features from images. Subsequently, we introduce a cross-modal alignment mechanism to semantically align textual event structures with visual features, ensuring high semantic consistency between the two modalities. Extensive experiments on multiple public datasets demonstrate that our method significantly outperforms existing unimodal and basic multimodal approaches, confirming its effectiveness and innovative contributions to social relation recognition.