personalitylinmult 2.1.4
PersonalityLinMulT: Transformer-based Big Five Automatic Personality Perception.
Requires Python: >=3.12
Keywords: app, bigfive, linear-complexity attention, multimodal, personality, transformer

# PersonalityLinMulT

[![License](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](LICENSE)
[![python](https://img.shields.io/badge/Python-3.11-3776AB.svg?style=flat&logo=python&logoColor=white)](https://www.python.org)
[![pytorch](https://img.shields.io/badge/PyTorch-2.5.1-EE4C2C.svg?style=flat&logo=pytorch)](https://pytorch.org)

LinMulT is trained for the Automatic Personality Perception (APP) task using the First Impressions V2 dataset to estimate perceived Big Five traits, and for Multimodal Sentiment Analysis (MSA) using the CMU-MOSI and CMU-MOSEI datasets to estimate sentiment from short video clips.

* paper: **Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures** ([pdf](https://proceedings.mlr.press/v173/fodor22a/fodor22a.pf), [website](https://proceedings.mlr.press/v173/fodor22a.html))

# Setup

### Install package from PyPI for inference

```
pip install personalitylinmult
```

### Install package for training

```
git clone https://github.com/fodorad/PersonalityLinMulT
cd PersonalityLinMulT
pip install -e .[all]
pip install -U -r requirements.txt
```

#### Supported extras definitions:

| extras tag | description                                                                               |
| ---------- | ----------------------------------------------------------------------------------------- |
| train      | dependencies for feature extraction, training the model from scratch and visualization    |
| all        | extends the 'train' dependencies for development. currently it is the same as 'train' tag |

# Related projects

### exordium

Collection of preprocessing functions and deep learning methods. This repository contains revised codes for fine landmark detection (including face, eye region, iris and pupil landmarks), head pose estimation, and eye feature calculation.

* code: https://github.com/fodorad/exordium

### (2022) LinMulT

General-purpose Multimodal Transformer with Linear Complexity Attention Mechanism. This base model is further modified and trained for various tasks and datasets.

* code: https://github.com/fodorad/LinMulT

### (2023) BlinkLinMulT

LinMulT is trained for blink presence detection and eye state recognition tasks.
Our results demonstrate comparable or superior performance compared to state-of-the-art models on 2 tasks, using 7 public benchmark databases.

* paper: BlinkLinMulT: Transformer-based Eye Blink Detection ([pdf](https://adamfodor.com/pdf/2023_Fodor_Adam_MDPI_BlinkLinMulT.pdf), [website](https://www.mdpi.com/2313-433X/9/10/196))
* code: https://github.com/fodorad/BlinkLinMulT

# Citation - BibTex

If you found our research helpful or influential please consider citing:

### (2023) BlinkLinMulT for blink presence detection and eye state recognition

```
@Article{fodor2023blinklinmult,
  title = {BlinkLinMulT: Transformer-Based Eye Blink Detection},
  author = {Fodor, Ádám and Fenech, Kristian and Lőrincz, András},
  journal = {Journal of Imaging},
  volume = {9},
  year = {2023},
  number = {10},
  article-number = {196},
  url = {https://www.mdpi.com/2313-433X/9/10/196},
  PubMedID = {37888303},
  ISSN = {2313-433X},
  DOI = {10.3390/jimaging9100196}
}
```

### (2022) LinMulT for personality trait and sentiment estimation

```
@InProceedings{pmlr-v173-fodor22a,
  title = {Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures},
  author = {Fodor, {\'A}d{\'a}m and Saboundji, Rachid R. and Jacques Junior, Julio C. S. and Escalera, Sergio and Gallardo-Pujol, David and L{\H{o}}rincz, Andr{\'a}s},
  booktitle = {Understanding Social Behavior in Dyadic and Small Group Interactions},
  pages = {218--241},
  year = {2022},
  editor = {Palmero, Cristina and Jacques Junior, Julio C. S. and Clapés, Albert and Guyon, Isabelle and Tu, Wei-Wei and Moeslund, Thomas B. and Escalera, Sergio},
  volume = {173},
  series = {Proceedings of Machine Learning Research},
  month = {16 Oct},
  publisher = {PMLR},
  pdf = {https://proceedings.mlr.press/v173/fodor22a/fodor22a.pdf},
  url = {https://proceedings.mlr.press/v173/fodor22a.html}
}
```

# Contact

* Ádám Fodor (foauaai@inf.elte.hu)