Update README.md

README.md

@@ -188,56 +188,6 @@ As MLLMs continue to advance, they remain largely focused on offline video compr
   <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