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

arXiv: https://arxiv.org/abs/2505.03846 · HTML: https://mlsanity.com/paper/2505.03846/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 7 of 7)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction](https://mlsanity.com/paper/2502.00631/) (2025)
- [Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features](https://mlsanity.com/paper/1610.10048/) (2016)
- [CSCE: Boosting LLM Reasoning by Simultaneous Enhancing of Causal Significance and Consistency](https://mlsanity.com/paper/2409.17174/) (2024)
- [Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition](https://mlsanity.com/paper/2409.09707/) (2024)
- [Efficient Knowledge Transfer in Multi-Task Learning through Task-Adaptive Low-Rank Representation](https://mlsanity.com/paper/2505.00009/) (2025)
- [Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment](https://mlsanity.com/paper/2504.11515/) (2025)

## Built on by (most cited first, 1 of 1)

- [GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation](https://mlsanity.com/paper/2505.05520/) (2025)

## Abstract (arXiv)

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.