Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- visual-question-answering
+- question-answering
+language:
+- en
+pretty_name: Multi-source Video Captioning
+size_categories:
+- 1K<n<10K
+---
+
+# Multi-source Video Captioning (MSVC) Dataset Card
+
+## Dataset details
+
+**Dataset type:**
+MSVC is a set of collected video captioning data. It is constructed to ensure a robust and thorough evaluation of Video-LLMs' video-captioning capabilities.
+
+**Dataset detail:**
+MSVC is introduced to address limitations in existing video caption benchmarks, MSVC samples 500 videos with human-annotated captions from [MSVD](https://www.aclweb.org/anthology/P11-1020/), [MSRVTT](http://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_MSR-VTT_A_Large_CVPR_2016_paper.pdf), and [VATEX](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_VaTeX_A_Large-Scale_High-Quality_Multilingual_Dataset_for_Video-and-Language_Research_ICCV_2019_paper.pdf), ensuring diverse scenarios and domains. 
+Traditional evaluation metrics rely on exact match statistics between generated and ground truth captions, which are limited in capturing the richness of video content. Thus, we use a ChatGPT-assisted evaluation similar to [VideoChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT/blob/main/quantitative_evaluation/README.md). Both generated and human-annotated captions are evaluated by GPT-3.5-turbo (0613) for Correctness of Information and Detailed Orientation.
+It is worth noting that each video in the MSVC benchmark is annotated with multiple human-written captions, covering different aspects of the video. This comprehensive annotation ensures a robust and thorough evaluation of Video-LLMs.
+
+**Data instructions**
+Please download the raw videos from their official websites and arrange them in the following structure:
+```bash
+VideoLLaMA2
+├── eval
+│   ├── MSVC
+|   |   ├── msvd/
+|   |   |    ├── lw7pTwpx0K0_38_48.avi
+|   |   |    └── ... 
+|   |   ├── msrvtt/
+|   |   |    ├── video9921.mp4
+|   |   |    └── ... 
+|   |   ├── vatex/
+|   |   |    ├── 9giWHf6Pf24.mp4
+|   |   |    └── ... 
+```
+
+
+**GPT3.5 Evaluation Prompt:**
+```python
+# Correctness evaluation:
+{
+    "role": "system",
+    "content": 
+        "You are an intelligent chatbot designed for evaluating the factual accuracy of generative outputs for video-based question-answer pairs. "
+        "Your task is to compare the predicted answer with these correct answers and determine if they are factually consistent. Here's how you can accomplish the task:"
+        "------"
+        "##INSTRUCTIONS: "
+        "- Focus on the factual consistency between the predicted answer and the correct answer. The predicted answer should not contain any misinterpretations or misinformation.\n"
+        "- The predicted answer must be factually accurate and align with the video content.\n"
+        "- Consider synonyms or paraphrases as valid matches.\n"
+        "- Evaluate the factual accuracy of the prediction compared to the answer."
+},
+{
+    "role": "user",
+    "content":
+        "Please evaluate the following video-based question-answer pair:\n\n"
+        f"Question: {question}\n"
+        f"Correct Answers: {answer}\n"
+        f"Predicted Answer: {pred}\n\n"
+        "Provide your evaluation only as a factual accuracy score where the factual accuracy score is an integer value between 0 and 5, with 5 indicating the highest level of factual consistency. "
+        "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is the factual accuracy score in INTEGER, not STRING."
+        "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+        "For example, your response should look like this: {''score': 4.8}."
+}
+```
+```python
+# Detailedness evaluation:
+{
+    "role": "system",
+    "content": "You are an intelligent chatbot designed for evaluating the detail orientation of generative outputs for video-based question-answer pairs. "
+    "Your task is to compare the predicted answer with these correct answers and determine its level of detail, considering both completeness and specificity. Here's how you can accomplish the task:"
+    "------"
+    "##INSTRUCTIONS: "
+    "- Check if the predicted answer covers all major points from the video. The response should not leave out any key aspects.\n"
+    "- Evaluate whether the predicted answer includes specific details rather than just generic points. It should provide comprehensive information that is tied to specific elements of the video.\n"
+    "- Consider synonyms or paraphrases as valid matches.\n"
+    "- Provide a single evaluation score that reflects the level of detail orientation of the prediction, considering both completeness and specificity.",
+},
+{
+    "role": "user",
+    "content": "Please evaluate the following video-based question-answer pair:\n\n"
+    f"Question: {question}\n"
+    f"Correct Answers: {answer}\n"
+    f"Predicted Answer: {pred}\n\n"
+    "Provide your evaluation only as a detail orientation score where the detail orientation score is an integer value between 0 and 5, with 5 indicating the highest level of detail orientation. "
+    "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is the detail orientation score in INTEGER, not STRING."
+    "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+    "For example, your response should look like this: {''score': 4.8}.",
+}
+```
+
+**Dataset date:**
+MSVC was released in June 2024.
+
+**Paper or resources for more information:**
+https://github.com/DAMO-NLP-SG/VideoLLaMA2
+
+**Where to send questions or comments about the model:**
+https://github.com/DAMO-NLP-SG/VideoLLaMA2/issues
+
+## Intended use
+**Primary intended uses:**
+The primary use of MSVC is research on Video-LLMs.
+
+**Primary intended users:**
+The primary intended users of the dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.