---
task_categories:
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
license: mit
---

# Towards Event-oriented Long Video Understanding

![VideoQA](https://img.shields.io/badge/Task-VideoQA-red) 
![Multi-Modal](https://img.shields.io/badge/Task-Multi--Modal-red) 

![Event-Bench](https://img.shields.io/badge/Dataset-EventBench-green) 
![VIM](https://img.shields.io/badge/Model-VIM-green)



<font size=3><div align='center' > [[📖 arXiv Paper]()]

---



## 👀 Overview

We introduce **Event-Bench**, an event-oriented long video understanding benchmark built on existing datasets and human annotations. **Event-Bench** consists of three event understanding abilities and six event-related tasks, including 2,190 test instances to comprehensively evaluate the ability to understand video events.
<p align="center">
    <img src="./asset/fig_benchmark.jpg" width="100%" height="100%">
</p>


**Event-Bench** provides a systematic comparison across different kinds of capabilities for existing video MLLMs, and points out the major shortcomings of open-source MLLMs.


## 🔍 Dataset
Download the raw videos in VNBench from the [google drive link](https://drive.google.com/file/d/1wjjH2dK-KpaObFdS1yc-TBUTCvXsaLwc/view?usp=sharing).

**License**:
```
Event-Bench is only used for academic research. Commercial use in any form is prohibited.
```


## 🔮 Evaluation Pipeline
Please refer to https://github.com/RUCAIBox/Event-Bench


## 📈 Experimental Results
- **Evaluation results of different Video MLLMs.**

<p align="center">
    <img src="./asset/performance.png" width="96%" height="50%">
</p>




## Citation

If you find our work helpful for your research, please consider citing our work.   

```bibtex

```