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README.md

@@ -1,3 +1,13 @@
+---
+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) 
@@ -5,24 +15,18 @@
 
 ![Event-Bench](https://img.shields.io/badge/Dataset-EventBench-green) 
 ![VIM](https://img.shields.io/badge/Model-VIM-green)
-<!-- <p align="center">
-    <img src="./asset/icon.png" width="15%" height="15%">
-</p> -->
 
 
 
-<font size=3><div align='center' > [[📖 arXiv Paper]()] [[📊 Dataset]()]  </div></font>
+<font size=3><div align='center' > [[📖 arXiv Paper]()]
 
 ---
 
-## 🔥 News
-* **`2024.06.20`** 🌟 Benchmark, evaluation code, training data, and model are released!
-
 
 
 ## 👀 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.
+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>
@@ -33,7 +37,7 @@ we introduce **Event-Bench**, an event-oriented long video understanding benchma
 
 ## 🔍 Dataset
 Download the raw videos in VNBench from the [google drive link](https://drive.google.com/file/d/1wjjH2dK-KpaObFdS1yc-TBUTCvXsaLwc/view?usp=sharing).
-Download the annotation of VNBench from the [huggingface link](https://huggingface.co/datasets/Richard1999/Event-Bench)
+
 **License**:
 ```
 Event-Bench is only used for academic research. Commercial use in any form is prohibited.
@@ -41,39 +45,9 @@ Event-Bench is only used for academic research. Commercial use in any form is pr
 
 
 ## 🔮 Evaluation Pipeline
-**Prompt**:
-
-The common prompt used in our evaluation follows this format:
-
-```
-<QUESTION>
-A. <OPTION1>
-B. <OPTION2>
-C. <OPTION3>
-D. <OPTION4>
-Answer with the option's letter from the given choices directly.
-```
+Please refer to https://github.com/RUCAIBox/Event-Bench
 
 
-
-**Evaluation**: 
-
-We recommend you to save the inference result in the format as [example_result.jsonl](./evaluation/example_result.jsonl). Once you have prepared the model responses in this format, please execute our evaluation script [evaluate_em.py](./evaluation/evaluate_em.py), and you will get the accuracy scores. 
-
-
-```bash
-python evaluate_em.py \
-    --path $RESULTS_FILE
-```
-
-If you want to use GPT-4-turbo for evaluation, please use the following script [evaluate_gpt.py](./evaluation/evaluate_gpt.py).
-
-```bash
-python evaluate_gpt.py \
-    --input_file $INPUT_FILE \
-    --output_file $OUTPUT_FILE 
-```
-
 ## 📈 Experimental Results
 - **Evaluation results of different Video MLLMs.**