Update README.md

README.md

@@ -150,5 +150,128 @@ configs:
       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. 🌟
+
+## 🎞️ 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>
+
+## 📐 Dataset Examples
+https://github.com/user-attachments/assets/e6d1655d-ab3f-47a7-973a-8fd6c8962307
+<div align="center">
+  <video width="100%" controls>
+    <source src="./figs/example.video" type="video/mp4">
+    Your browser does not support the video tag.
+  </video>
+</div>
+
+## 🔮 Evaluation Pipeline
+
+### Requirements
+
+- Python 3.x
+- moviepy
+
+### Data Preparation
+
+1. **Download Dataset**: Retrieve all necessary files from the [StreamingBench Dataset](https://huggingface.co/datasets/mjuicem/StreamingBench).
+   
+2. **Decompress Files**: Extract the downloaded files and organize them in the `./data` directory as follows:
+
+   ```
+   StreamingBench/
+   ├── data/
+   │   ├── real/               # Unzip Real Time Visual Understanding_*.zip into this folder
+   │   ├── omini/              # Unzip other .zip files into this folder
+   │   ├── sqa/                # Unzip Sequential Question Answering_*.zip into this folder
+   │   └── proactive/          # Unzip Proactive Output_*.zip into this folder
+   ```
+
+3. **Preprocess Data**: Run the following command to preprocess the data:
+
+   ```bash
+   bash scripts/preprocess.sh
+   ```
+
+### Model Preparation
+
+Prepare your own model for evaluation by following the instructions provided [here](./docs/model_guide.md). This guide will help you set up and configure your model to ensure it is ready for testing against the dataset.
+
+### Evaluation
+
+Now you can run the benchmark:
+
+```sh
+bash eval.sh
+```
+
+This will run the benchmark and save the results to the specified output file.
+
+## 🔬 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