Datasets:
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---
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">๐ Project Page</a> |
<a href="https://arxiv.org/abs/2411.03628" style="margin: 0 10px">๐ arXiv Paper</a> |
<a href="https://huggingface.co./datasets/mjuicem/StreamingBench" style="margin: 0 10px">๐ฆ Dataset</a> |
<a href="https://streamingbench.github.io/#leaderboard" style="margin: 0 10px">๐
Leaderboard</a>
</div>
</div>
**StreamingBench** evaluates **Multimodal Large Language Models (MLLMs)** in real-time, streaming video understanding tasks. ๐
## ๐๏ธ 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
- ๐ฏ **Real-time Visual Understanding**: Can the model process and respond to visual changes in real-time?
- ๐ **Omni-source Understanding**: Does the model integrate visual and audio inputs synchronously in real-time video streams?
- ๐ฌ **Contextual Understanding**: Can the model comprehend the broader context within video streams?
### Dataset Statistics
- ๐ **900** diverse videos
- ๐ **4,500** human-annotated QA pairs
- โฑ๏ธ Five questions per video at different timestamps
#### ๐ฌ Video Categories
<div align="center">
<img src="./figs/StreamingBench_Video.png" width="80%" alt="Video Categories">
</div>
#### ๐ Task Taxonomy
<div align="center">
<img src="./figs/task_taxonomy.png" width="80%" alt="Task Taxonomy">
</div>
## ๐ฌ 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
*"โค 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>
## ๐ 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
|