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---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: video-text-to-text
---
# R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning
[](https://modelscope.cn/models/iic/R1-Omni-0.5B)
[](https://huggingface.co/StarJiaxing/R1-Omni-0.5B)
[](https://arxiv.org/abs/2503.05379)
This model utilizes Reinforcement Learning with Verifiable Reward (RLVR) to perform omni-multimodal emotion recognition. Built upon the HumanOmni-0.5B model, R1-Omni excels at understanding visual and audio cues for emotion identification, even in out-of-distribution scenarios.
## 📖 Introduction
**R1-Omni** is the first application of Reinforcement Learning with Verifiable Reward (RLVR) to an Omni-multimodal large language model. It focuses on emotion recognition, where visual and audio modalities play crucial roles. Key insights include:
1) **Enhanced Reasoning Capability**: R1-Omni demonstrates superior reasoning abilities, enabling a clearer understanding of how visual and audio information contribute to emotion recognition.
2) **Improved Understanding Capability**: Compared to SFT, RLVR significantly boosts performance on emotion recognition tasks.
3) **Stronger Generalization Capability**: RLVR models exhibit markedly better generalization capabilities, particularly excelling in out-of-distribution scenarios.
## 📦 Model Download
The model is based on the open-source HumanOmni-0.5B model. The following models are available: HumanOmni-0.5B, the cold-start model EMER-SFT, the model MAFW-DFEW-SFT fine-tuned directly on the MAFW and DFEW training sets, and the final model R1-Omni.
<div align="center">
| **Model** | **HuggingFace** | **ModelScope** |
|------------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------|
| `HumanOmni-0.5B` | [](https://hf.co/StarJiaxing/HumanOmni-0.5B) | [](https://modelscope.cn/models/iic/HumanOmni-0.5B) |
| `EMER-SFT` | [](https://hf.co/StarJiaxing/EMER-SFT-0.5B) | [](https://modelscope.cn/models/iic/EMER-SFT-0.5B) |
| `MAFW-DFEW-SFT` | [](https://huggingface.co/StarJiaxing/MAFW-DFEW-0.5B) | [](https://modelscope.cn/models/iic/MAFW-DFEW-0.5B) |
| `R1-Omni` | [](https://huggingface.co/StarJiaxing/R1-Omni-0.5B) | [](https://modelscope.cn/models/iic/R1-Omni-0.5B) |
</div>
## 🏆 Performance
Below are the performance on emotion recognition datasets. We use symbols to indicate whether the data is **in-distribution (⬤)** or **out-of-distribution (△)**.
| Method | DFEW (WAR) ⬤ | DFEW (UAR) ⬤ | MAFW (WAR) ⬤ | MAFW (UAR) ⬤ | RAVDESS (WAR) △ | RAVDESS (UAR) △ |
|----------------------------------|---------------|---------------|---------------|---------------|------------------|------------------|
| HumanOmni-0.5B | 22.64 | 19.44 | 20.18 | 13.52 | 7.33 | 9.38 |
| EMER-SFT | 38.66 | 35.31 | 38.39 | 28.02 | 29.00 | 27.19 |
| MAFW-DFEW-SFT | 60.23 | 44.39 | 50.44 | 30.39 | 29.33 | 30.75 |
| R1-Omni | 65.83 | 56.27 | 57.68 | 40.04 | 43.00 | 44.69 |
### Legend
- **⬤**: Indicates **in-distribution data** (DFEW and MAFW).
- **△**: Indicates **out-of-distribution data** (RAVDESS).
## 🛠️ Environment Setup
Our code is built on the R1-V framework. To set up the environment, please follow the installation instructions in the [R1-V repository](https://github.com/Deep-Agent/R1-V/)
## 🔍 Inference
Our inference code is based on the implementation from **HumanOmni**.
## 📚 Citation
If you find our work helpful, feel free to cite us.
```
{zhao2025r1omniexplainableomnimultimodalemotion,
title={R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning},
author={Jiaxing Zhao and Xihan Wei and Liefeng Bo},
journal={arXiv preprint arXiv:2503.05379},
year={2025}
}
``` |