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


dataset_info:
  - config_name: Real_Time_Visual_Understanding
    features:
      - name: question_id
        dtype: string
      - name: task_type
        dtype: string
      - name: question
        dtype: string
      - name: time_stamp
        dtype: string
      - name: answer
        dtype: string
      - name: options
        dtype: string
      - name: frames_required
        dtype: string
      - name: temporal_clue_type
        dtype: string
    splits:
      - name: Real_Time_Visual_Understanding
        num_examples: 2500

  - config_name: Sequential_Question_Answering
    features:
      - name: question_id
        dtype: string
      - name: task_type
        dtype: string
      - name: question
        dtype: string
      - name: time_stamp
        dtype: string
      - name: answer
        dtype: string
      - name: options
        dtype: string
      - name: frames_required
        dtype: string
      - name: temporal_clue_type
        dtype: string
    splits:
      - name: Sequential_Question_Answering
        num_examples: 250


  - config_name: Contextual_Understanding
    features:
      - name: question_id
        dtype: string
      - name: task_type
        dtype: string
      - name: question
        dtype: string
      - name: time_stamp
        dtype: string
      - name: answer
        dtype: string
      - name: options
        dtype: string
      - name: frames_required
        dtype: string
      - name: temporal_clue_type
        dtype: string
    splits:
      - name: Contextual_Understanding
        num_examples: 500

  - config_name: Omni_Source_Understanding
    features:
      - name: question_id
        dtype: string
      - name: task_type
        dtype: string
      - name: question
        dtype: string
      - name: time_stamp
        dtype: string
      - name: answer
        dtype: string
      - name: options
        dtype: string
      - name: frames_required
        dtype: string
      - name: temporal_clue_type
        dtype: string
    splits:
      - name: Omni_Source_Understanding
        num_examples: 1000

    
  - config_name: Proactive_Output
    features:
      - name: question_id
        dtype: string
      - name: task_type
        dtype: string
      - name: question
        dtype: string
      - name: time_stamp
        dtype: string
      - name: ground_truth_time_stamp
        dtype: string
      - name: ground_truth_output
        dtype: string
      - name: frames_required
        dtype: string
      - name: temporal_clue_type
        dtype: string
    splits:
      - name: Proactive_Output
        num_examples: 250


configs:
  - config_name: Real_Time_Visual_Understanding
    data_files:
    - split: Real_Time_Visual_Understanding
      path: StreamingBench/Real_Time_Visual_Understanding.csv 
      
  - config_name: Sequential_Question_Answering
    data_files:
    - split: Sequential_Question_Answering
      path: StreamingBench/Sequential_Question_Answering.csv

  - config_name: Contextual_Understanding
    data_files:
    - split: Contextual_Understanding
      path: StreamingBench/Contextual_Understanding.csv
      
  - config_name: Omni_Source_Understanding
    data_files:
    - split: Omni_Source_Understanding
      path: StreamingBench/Omni_Source_Understanding.csv
      
  - config_name: Proactive_Output
    data_files:
    - split: Proactive_Output
      path: StreamingBench/Proactive_Output_50.csv
    - split: Proactive_Output_250
      path: StreamingBench/Proactive_Output.csv
    
---
# StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding

<div align="center">
  <img src="./figs/icon.png" width="100%" alt="StreamingBench Banner">

  <div style="margin: 30px 0">
    <a href="https://streamingbench.github.io/" style="margin: 0 10px">🏠 Project Page</a> |
    <a href="https://arxiv.org/abs/2411.03628" style="margin: 0 10px">📄 arXiv Paper</a> |
    <a href="https://huggingface.co/datasets/mjuicem/StreamingBench" style="margin: 0 10px">📦 Dataset</a> |
    <a href="https://streamingbench.github.io/#leaderboard" style="margin: 0 10px">🏅Leaderboard</a>
  </div>
</div>

**StreamingBench** evaluates **Multimodal Large Language Models (MLLMs)** in real-time, streaming video understanding tasks. 🌟

------

[**NEW!** 2025.05.15] 🔥: [Seed1.5-VL](https://github.com/ByteDance-Seed/Seed1.5-VL) achieved ALL model SOTA with a score of 82.80 on the Proactive Output.

[**NEW!** 2025.03.17] ⭐: [ViSpeeker](https://arxiv.org/abs/2503.12769) achieved Open-Source SOTA with a score of 61.60 on the Omni-Source Understanding.

[**NEW!** 2025.01.14] 🚀: [MiniCPM-o 2.6](https://github.com/OpenBMB/MiniCPM-o) achieved Streaming SOTA with a score of 66.01 on the Overall benchmark.

[**NEW!** 2025.01.06] 🏆: [Dispider](https://github.com/Mark12Ding/Dispider) achieved Streaming SOTA with a score of 53.12 on the Overall benchmark.

[**NEW!** 2024.12.09] 🎉: [InternLM-XComposer2.5-OmniLive](https://github.com/InternLM/InternLM-XComposer) achieved 73.79 on Real-Time Visual Understanding.

------

## 🎞️ Overview

As MLLMs continue to advance, they remain largely focused on offline video comprehension, where all frames are pre-loaded before making queries. However, this is far from the human ability to process and respond to video streams in real-time, capturing the dynamic nature of multimedia content. To bridge this gap, **StreamingBench** introduces the first comprehensive benchmark for streaming video understanding in MLLMs.

### Key Evaluation Aspects
- 🎯 **Real-time Visual Understanding**: Can the model process and respond to visual changes in real-time?
- 🔊 **Omni-source Understanding**: Does the model integrate visual and audio inputs synchronously in real-time video streams?
- 🎬 **Contextual Understanding**: Can the model comprehend the broader context within video streams?

### Dataset Statistics
- 📊 **900** diverse videos
- 📝 **4,500** human-annotated QA pairs
- ⏱️ Five questions per video at different timestamps
#### 🎬 Video Categories
<div align="center">
  <img src="./figs/StreamingBench_Video.png" width="80%" alt="Video Categories">
</div>

#### 🔍 Task Taxonomy
<div align="center">
  <img src="./figs/task_taxonomy.png" width="80%" alt="Task Taxonomy">
</div>


## 🔬 Experimental Results

### Performance of Various MLLMs on StreamingBench
- All Context
<div align="center">
  <img src="./figs/result_1.png" width="80%" alt="Task Taxonomy">
</div>

- 60 seconds of context preceding the query time
<div align="center">
  <img src="./figs/result_2.png" width="80%" alt="Task Taxonomy">
</div>

- Comparison of Main Experiment vs. 60 Seconds of Video Context
- <div align="center">
  <img src="./figs/heatmap.png" width="80%" alt="Task Taxonomy">
</div>

### Performance of Different MLLMs on the Proactive Output Task
*"≤ xs" means that the answer is considered correct if the actual output time is within x seconds of the ground truth.*
<div align="center">
  <img src="./figs/po.png" width="80%" alt="Task Taxonomy">
</div>


## 📝 Citation
```bibtex
@article{lin2024streaming,
  title={StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding},
  author={Junming Lin and Zheng Fang and Chi Chen and Zihao Wan and Fuwen Luo and Peng Li and Yang Liu and Maosong Sun},
  journal={arXiv preprint arXiv:2411.03628},
  year={2024}
}
```

https://arxiv.org/abs/2411.03628