GAME: Learning Multimodal Interactions via Graph Structures for Personality Trait Estimation

• URL: http://arxiv.org/abs/2505.03846v2
• Date: Sat, 31 May 2025 09:08:51 GMT
• Title: GAME: Learning Multimodal Interactions via Graph Structures for Personality Trait Estimation
• Authors: Kangsheng Wang, Yuhang Li, Chengwei Ye, Yufei Lin, Huanzhen Zhang, Bohan Hu, Linuo Xu, Shuyan Liu,
• Abstract summary: Apparent personality analysis from short videos poses significant chal-lenges due to the complex interplay of visual, auditory, and textual cues.<n>In this paper, we propose GAME, a Graph-Augmented Multimodalvolution are designed to robustly model and fuse multi-source features for automatic personality prediction.<n>For the visual stream, we construct a facial graph and introduce a dual-branch Geo Two-Stream Network, which combines Graph Convolutional Networks (GCNs) and Convolutional Neural Net-works (CNNs)<n>To capture temporal dynamics, frame-level features are processed by a BiG
• Score: 13.071227081328288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Apparent personality analysis from short videos poses significant chal-lenges due to the complex interplay of visual, auditory, and textual cues. In this paper, we propose GAME, a Graph-Augmented Multimodal Encoder designed to robustly model and fuse multi-source features for automatic personality prediction. For the visual stream, we construct a facial graph and introduce a dual-branch Geo Two-Stream Network, which combines Graph Convolutional Networks (GCNs) and Convolutional Neural Net-works (CNNs) with attention mechanisms to capture both structural and appearance-based facial cues. Complementing this, global context and iden-tity features are extracted using pretrained ResNet18 and VGGFace back-bones. To capture temporal dynamics, frame-level features are processed by a BiGRU enhanced with temporal attention modules. Meanwhile, audio representations are derived from the VGGish network, and linguistic se-mantics are captured via the XLM-Roberta transformer. To achieve effective multimodal integration, we propose a Channel Attention-based Fusion module, followed by a Multi-Layer Perceptron (MLP) regression head for predicting personality traits. Extensive experiments show that GAME con-sistently outperforms existing methods across multiple benchmarks, vali-dating its effectiveness and generalizability.

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