# Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment

arXiv: https://arxiv.org/abs/2504.11515 · HTML: https://mlsanity.com/paper/2504.11515/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [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)
- [Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition](https://mlsanity.com/paper/1609.05119/) (2016)
- [Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features](https://mlsanity.com/paper/1610.10048/) (2016)
- [On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals](https://mlsanity.com/paper/1909.05568/) (2019)
- [Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos](https://mlsanity.com/paper/1802.00745/) (2018)
- [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)
- [Multimodal Personality Recognition using Cross-Attention Transformer and Behaviour Encoding](https://mlsanity.com/paper/2112.12180/) (2021)
- [CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs](https://mlsanity.com/paper/2410.01696/) (2024)

## Built on by (most cited first, 2 of 2)

- [GAME: Learning Multimodal Interactions via Graph Structures for Personality Trait Estimation](https://mlsanity.com/paper/2505.03846/) (2025)
- [GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation](https://mlsanity.com/paper/2505.05520/) (2025)

## Abstract (arXiv)

Predicting personality traits automatically has become a challenging problem in computer vision. This paper introduces an innovative multimodal feature learning framework for personality analysis in short video clips. For visual processing, we construct a facial graph and design a Geo-based two-stream network incorporating an attention mechanism, leveraging both Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to capture static facial expressions. Additionally, ResNet18 and VGGFace networks are employed to extract global scene and facial appearance features at the frame level. To capture dynamic temporal information, we integrate a BiGRU with a temporal attention module for extracting salient frame representations. To enhance the model's robustness, we incorporate the VGGish CNN for audio-based features and XLM-Roberta for text-based features. Finally, a multimodal channel attention mechanism is introduced to integrate different modalities, and a Multi-Layer Perceptron (MLP) regression model is used to predict personality traits. Experimental results confirm that our proposed framework surpasses existing state-of-the-art approaches in performance.